Analysis and screening of cell secretion profiles

ABSTRACT

Embodiments disclose apparatus, methods and software for performing biological screening and analysis implemented using an instrument platform capable of detecting a wide variety of cell-based secretions, expressed proteins, and other cellular components. The platform may be configured for simultaneous multiplexed detection of a plurality biological components such that a large number of discrete samples may be individually sequestered and evaluated to detect or identify constituents from the samples in a highly parallelized and scalable manner.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/773,535, filed Jan. 27, 2020, now issued as U.S.Pat. No. 11,066,689, which is a continuation application of U.S. patentapplication Ser. No. 15/532,428, filed Jun. 1, 2017, now issued as U.S.Pat. No. 10,584,366, which is a National Stage Application, filed under35 U.S.C § 371, of International Application No. PCT/US2015/063754 filedDec. 3, 2015, which claims the benefit of priority to U.S. ProvisionalPatent Application Ser. No. 62/087,147 filed Dec. 3, 2014, the contentsof each of which are incorporated by reference herein in their entirety.

FIELD OF THE DISCLOSURE

The present invention generally relates to cellular analysis, and moreparticularly to apparatus, methods, and software for biochemicalassessment and functional characterization of cellular states.

BACKGROUND

In fields including cell biology and immunology, development ofanalytical techniques for evaluation and quantitation of cellularprotein expression and secretion profiles is of significant importanceto elucidate underlying biochemical processes and cell signalingmechanisms. Due in part to the heterogeneous behaviors often exhibitedby cells, a need exists for tools and procedures capable of assayinglarge numbers of discrete cell populations that are also suitable fordetection of biomolecules at the single cell level. Sensitive andaccurate assessment of cellular phenotypes and functionalities as wellas identification of drivers and interactions between individual cellshave been shown to be important indicia of the capabilities andoperation of biological systems.

As one example, immune cell response is directed by a large number ofsecreted proteins including cytokines, chemokines, and growth factorswhich represent important functional regulators mediating a range ofcellular behaviors and cell-cell signaling processes. Monitoring thesecomplex immuno-signaling pathways and cellular interactions presentsignificant challenges to identifying clinically relevant measurementsthat can be used to understand that state of the immune system, predictclinical outcomes, and direct treatment or therapies. Increasingly,there is a demand for sensitive and highly-multiplexed technologies forcellular analysis that can be used to identify and rapidly evaluatecorrelatives of disease, cellular responses to various chemicals andtherapeutic agents, and other cell-based processes involved inimmunological interventions. Such technologies can also be used tobetter understand the underlying mechanisms of immunity.

SUMMARY OF THE DISCLOSURE

The present disclosure provides apparatus, methods and software usefulfor determining and investigating cellular processes and functionalprofiles. In various embodiments, the present teachings may beadvantageously applied in the context of cellular analysis of immunecells. For example, T-cell functionality and correlative response tovarious therapies, chemical interactions and/or disease states may beassessed at the single cell level.

In various embodiments, apparatus, methods and software are disclosedfor performing biological screening and analysis implemented using aninstrument platform capable of detecting a wide variety of cell-basedsecretions, expressed proteins, and other cellular components. Theplatform may be configured for simultaneous multiplexed detection of aplurality of biological components such that a large number of discretesamples may be individually sequestered and evaluated to detect oridentify constituents from the samples in a highly parallelized andscalable manner. In various embodiments, the platform is configured forautomated or semi-automated processing significantly improvingtime-to-result, sample throughput, detection accuracy, and sensitivity.

The platform may be advantageously adapted for use in applications toanalyze small numbers of cells and single cells. As disclosed herein,analysis of single cells and cellular interactions between single cells(e.g. cell-cell interactions) is particularly useful in immunologicalapplications to aid in the determination of functional profiles forimmuno cells such as B-cells, T cells (e.g. CD4+, CD8+ cells), and/ormacrophage cells. While various examples and workflows are provided anddiscussed involving immuno cells, it will be appreciated that theapparatus, methods and software of the present teachings may be adaptedfor use with a wide variety of different cell types. Furthermore, thesample analysis techniques may be extended outside of cellular orbiological analysis applications to be used in other chemical surveyswhere multiple discrete samples are desirably evaluated substantiallysimultaneously for a plurality of different analytes. Additionally,sample constituents other than cells may be evaluated in parallel, forexample, beads or other particles containing chemicals, compounds orother analytes of interest. In various embodiments, the technologiesdisclosed herein are sufficiently sensitive to detect, distinguish, andquantify multiple analytes present in very small liquid or aqueousvolumes (for example, from nanoliter and picoliter volumes or less).

In various embodiments, a system is described for discretely resolvinganalytes associated with a cellular population comprising: (a) aplurality of sample retention regions that receive at least one celldistributed from a population of cells and retain an associatedplurality of analytes released by the at least one cell; (b) a pluralityof analyte detection regions patterned with a plurality of discretelypositioned analyte detection moieties, the analyte detection regionsdisposed in an ordered pattern alignable with the sample retentionregions whereby, upon coupling, released analytes selectively associatewith the plurality of analyte detection moieties forming an analytepattern for each analyte detection region; (c) a plurality of firstalignment markers disposed about the plurality of sample retentionregions and a plurality of second alignment markers disposed about theplurality of analyte detection regions; and (d) an imaging apparatusthat generates a plurality of images for selected sample retentionregions and retained at least one cell and additionally at least onefirst alignment marker, the imaging apparatus further generating aplurality of images for analyte detection regions corresponding to theselected sample regions and associated analyte patterns and additionallyat least one second alignment marker; and an image processor that alignsassociated images for selected sample retention regions using the atleast one first alignment marker and further aligns images for analytedetection regions with corresponding images for selected sampleretention regions using the at least one second alignment marker, theimage processor further identifying retained at least one cell inrespective sample retention regions and corresponding analyte patternsto discretely resolve released analytes associated with the retained atleast one cell based on the analyte detection moieties detected in theanalyte pattern.

In other embodiments, the a method is described for discretely resolvinganalytes associated with a cellular population comprising: (a) acquiringa plurality of images for a plurality of sample retention regions thatreceive at least one cell distributed from a population of cells andretain an associated plurality of analytes released by the at least onecell; (b) acquiring a plurality of images for a plurality of analytedetection regions patterned with a plurality of discretely positionedanalyte detection moieties, the analyte detection regions disposed in anordered pattern alignable with the sample retention regions whereby,upon coupling, released analytes selectively associate with theplurality of analyte detection moieties forming an analyte pattern foreach analyte detection region; (c) resolving a plurality of firstalignment markers disposed about the plurality of sample retentionregions; (d) resolving a plurality of second alignment markers disposedabout the plurality of analyte detection regions; (e) aligningassociated images for selected sample retention regions using at leastone of the plurality of first alignment markers and further aligningimages for analyte detection regions with corresponding images forselected sample retention regions using the at least one of theplurality of second alignment markers; and (f) identifying retained atleast one cell in respective sample retention regions and correspondinganalyte patterns to discretely resolve released analytes associated withthe retained at least one cell based on the analyte detection moietiesdetected in the analyte pattern.

In further embodiments, described is non-transitory computer-readablemedia having computer executable code stored thereon for discretelyresolving analytes associated with a cellular population, the codecomprising: (a) an executable routine for acquiring a plurality ofimages for a plurality of sample retention regions that receive at leastone cell distributed from a population of cells and retain an associatedplurality of analytes released by the at least one cell; (b) anexecutable routine for acquiring a plurality of images for a pluralityof analyte detection regions patterned with a plurality of discretelypositioned analyte detection moieties, the analyte detection regionsdisposed in an ordered pattern alignable with the sample retentionregions whereby, upon coupling, released analytes selectively associatewith the plurality of analyte detection moieties forming an analytepattern for each analyte detection region; (c) an executable routine forresolving a plurality of first alignment markers disposed about theplurality of sample retention regions; (d) an executable routine forresolving a plurality of second alignment markers disposed about theplurality of analyte detection regions; (e) an executable routine foraligning associated images for selected sample retention regions usingat least one of the plurality of first alignment markers and furtheraligning images for analyte detection regions with corresponding imagesfor selected sample retention regions using the at least one of theplurality of second alignment markers; and (f) an executable routine foridentifying retained at least one cell in respective sample retentionregions and corresponding analyte patterns to discretely resolvereleased analytes associated with the retained at least one cell basedon the analyte detection moieties detected in the analyte pattern.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims. It is tobe understood that both the foregoing general description and thefollowing detailed description are exemplary and explanatory only andare not restrictive of the scope of disclosed embodiments, as set forthby the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other embodiments of the disclosure will be discussed withreference to the following exemplary and non-limiting illustrations, inwhich like elements are numbered similarly, and where:

FIG. 1A depicts an exemplary high-level workflow for performing sampleanalysis according to the present disclosure.

FIG. 1B illustrates an exemplary high-level workflow for performingsample analysis according to the present disclosure.

FIG. 1C depicts an exemplary sample array and an analyte detectionsubstrate with magnified sub-portions according to the presentdisclosure.

FIG. 1D depicts an exemplary detailed analysis workflow for imaging andresolution of sample array data according to the present disclosure.

FIG. 1E depicts an exemplary outline of processes associated withmultiple imaging of sub-regions of a sample array according to thepresent disclosure.

FIG. 1F depicts exemplary imaging of a selected sub-regions of samplearray and cells/particulates according to the present disclosure.

FIG. 1G depicts exemplary signal scans for analyte patterns andalignment markers according to the present disclosure.

FIG. 1H depicts exemplary light field and fluorescent image panels for asample array with associated image panel for a corresponding analytedetection substrate and the merging of the images panels according tothe present disclosure.

FIG. 2 depicts an exemplary process for sample retention region locationdetermination and resolution according to the present disclosure.

FIG. 3 depicts an exemplary method for location of alignment featuresaccording to the present disclosure.

FIG. 4 illustrates exemplary operation of a sample retention regionidentification process according to the present disclosure.

FIG. 5 depicts exemplary processes for alignment feature location andresolution according to the present disclosure.

FIG. 6A-1 and FIG. 6A-2 depicts an exemplary cell/particulate locationidentification process according to the present disclosure.

FIG. 6B illustrates an exemplary cell detection and visualization methodfor sample region boundaries according to the present disclosure.

FIG. 6C depicts an exemplary cell template matching processes accordingto the present disclosure.

FIG. 6D depicts an exemplary cell template matching processes accordingto the present disclosure.

FIG. 6E depicts an exemplary process for generating a cell/particulatetemplate library according to the present disclosure.

FIG. 7A depicts an exemplary workflow for alignment of images and scansaccording to the present disclosure.

FIG. 7B illustrates exemplary operations applied to exemplary images andscans using alignment markers according to the present disclosure.

FIG. 7C depicts an exemplary method for first and second image alignmentaccording to the present disclosure.

FIG. 7D illustrates an exemplary process for first and second imagealignment according to the present disclosure.

FIG. 8A depicts an exemplary process 800 for association and alignmentof features according to the present disclosure.

FIG. 8B illustrates an exemplary process for association and alignmentof features according to the present disclosure.

FIG. 9A-1 and FIG. 9A-2 depicts an exemplary method for cell/particulateposition identification and discrimination according to the presentdisclosure.

FIG. 9B illustrates an exemplary positioning and discrimination processfor cells labeled with surface markers according to the presentdisclosure.

FIG. 10A depicts an exemplary process for merging sample retentionregion images/analyte scans according to the present disclosure.

FIG. 10B illustrates exemplary imaging and pixel comparison processesaccording to the present disclosure.

FIG. 10C-1 and FIG. 10C-2 illustrates an example of an imaging and pixelcomparison process showing pairwise readouts associated with markerpatterns for exemplary sample retention regions according to the presentdisclosure.

FIG. 11 depicts an exemplary process for gating outlier data and signalsaccording to the present disclosure.

FIG. 12A illustrates exemplary images of a portion of a sample retentionregion with intersecting analyte detection regions according to thepresent disclosure.

FIG. 12B depicts an exemplary scatterplot for evaluating a plurality ofanalytes according to the present disclosure.

FIG. 12C illustrates exemplary imaging results for markerpatterns/readouts from detected analytes associated with two cells celltypes according to the present disclosure.

FIG. 13A depicts exemplary methods and tools for presenting andinterpreting analyte data and results according to the presentdisclosure. FIG. 13B depicts exemplary methods and tools for presentingand interpreting analyte data and results according to the presentdisclosure.

FIG. 14A depicts an exemplary database interface for evaluatingexperimental results according to the present disclosure.

FIG. 14B depicts an exemplary results interface for displayinginformation from an experiment according to the present disclosure.

FIG. 14C depicts a workflow for analysis functionalities and databasequerying according to the present disclosure.

FIG. 14D depicts an exemplary platform comprising components for imageacquisition and computational components for image analysis andsubsequent analyte evaluation according to the present disclosure.

DETAILED DESCRIPTION

Efforts to characterize the state or capabilities of a biological systemor sample may be confounded or obscured by bulk analysis methods. Thisis evident in cellular analysis such as immunological surveys wherein asample comprising many cells analyzed in a common volume or medium mayact to mask or dilute analytes. It is therefore desirable in manyinstances to provide very small volume and single cell sample analysiscapabilities. For small sample volumes including those involving asingle or a few cells it is further desirable to sequester multiplesamples in discrete reaction regions or areas so they may be subjectedto parallel analysis. Such methods may require avoiding sample-to-samplecross-talk and contamination while providing the ability to subject thesamples to uniform and discrete treatment. Collection of accurate andsensitive data in a scalable and multiplexed format with minimal userintervention is also important.

A particular example of a biochemical analysis particularly amenable todiscrete cellular analysis relates to the evaluation of functionalstates based on secreted proteins from individual cells or arising fromcell-to-cell interactions. In a model system comprising immune cellanalysis, cell protein or chemical secretion profiles can be correlatedwith the quality of immune response at the single-cell level. Forexample, multiple cytokines may define or distinguish thedifferentiation stages in both CD4 and CD8 cell populations. Further,central memory and memory/effector cells may be associated withincreased probabilities of secreting particular cytokines such as tumornecrosis factor alpha (TNF-α), interleukin 2 (IL2) and/or interferongamma (IFN-γ). In contrast, cells that principally produce and secreteIFN-γ may be more likely to exhibit terminally differentiated effectorsassociated with programmed cell death (e.g., apoptosis).

To facilitate screening for a multiplicity of potential functionalphenotypes such as those demonstrated in CD4 cells as well as to allowmeasurement of poly-functionality such as that associated with cellularimmune response it is desirable to analyze multiple analytessimultaneously. For such analysis it is further desirable to enabledetection of multiple analytes substantially simultaneously fromindividual or few numbers of cells where many hundreds, thousands,millions, or more discrete cells, samples, and/or discrete cellularinteractions are evaluated for multiple analytes. Furthermore, thenumber of analytes that are screened for each cell or sample populationmay be between relatively few (for example between approximately 1-5analytes) or more (for example between approximately 5 and 100 analytes)and perhaps even greater (for example more than 100 analytes) for eachsample.

In a highly multiplexed system where many discrete samples may bedesirably evaluated for many different analytes, a particular challengearises in terms of quickly and accurately acquiring the associated dataand performing high quality analysis. The small scale and large numberof discrete samples to be simultaneously analyzed by such systems maymake it impractical to perform these analyses adequately without a highdegree of automation and/or user-independent computational analysis.This is particularly evident for developing single-cell technologies andcellular analysis systems intended to evaluate many hundreds, thousandsor more discrete samples.

Exemplary single-cell analysis technologies have recently been describedfor example in “High-Throughput Secretomic Analysis of Single Cells toAssess Functional Cellular Heterogeneity” Analytical Chemistry, 2013,Volume 85, Pages 2548-2556, “Microfluidics-Based Single-Cell FunctionalProteomics for Fundamental and Applied Biomedical Applications,” AnnualReview of Analytical Chemistry, 2014, Volume 7, Pages 275-95, and“Single-cell technologies for monitoring immune systems,” NatureImmunology, 2014, Volume 15, Number 2, Pages 128-135 secreted proteinsmay be measured on a per-cell basis. Additionally, exemplary apparatusand platforms for single cell and few cell analyses are disclosed in PCTApplication Serial Number PCT/US2013/056454 (PCT Patent PublicationWO2014031997), U.S. patent application Ser. No. 12/174,601 (US PatentPublication 2009/0036324), U.S. patent application Ser. No. 12/174,598(US Patent Publication 2009/0053732), U.S. patent application Ser. No.12/857,510 (U.S. Pat. No. 8,865,479), and U.S. patent application Ser.No. 13/132,858 (US Patent Publication 2012/0015824).

As will be described in greater detail hereinbelow, systems such as theaforementioned single cell analysis and few-cell analyses platforms andtechnologies may be adapted to benefit from the automated andsemi-automated analysis methods of the present teachings to improvethroughput, accuracy, inference discovery and results confidence.Furthermore, the apparatus, methods, and software of the presentteachings may facilitate analysis of very large numbers of discretesamples when performing multiplexed chemical, biochemical, or cellularsecretion analysis including those where multiple analytes are detected,identified, and quantified for each sample.

According to various embodiments, the disclosed detection methods may beused to analyze a wide variety of compounds including for examplebiochemicals, cellular secretions and/or expressed proteins associatedwith a biological system (for example, immunological cells) in amultiplexed manner. Additionally, a multiplicity of different proteinsand/or compounds associated with a single or few cells (includingcell-cell interactions) may be detected and analyzed to generate acellular secretion or expression profile reflecting the response orstatus of a representative cellular population, cellular sub-population,or single cell for a subject or sample.

In the context of immunological analysis, due to the phenotypic andfunctional heterogeneity of immuno cells and the plasticity of immunecell differentiation, conventional methods that analyze cells in bulkcreate difficulties in defining or identifying correlates of immuneprotection against diseases such as cancers and infectious agents suchas pathogens. Secretion and expression profiles associated with immuneprotection are potentially valuable and measurable predictors of anindividual's immunity. Such profiles may be used to evaluate andquantitate response to a pathogen, disease, or treatment and are helpfultools in clinical analysis.

Correlates of protective immunity (for example associated with T cells)have been particularly challenging for immunologists to identify atleast in part as the degree of protection may not clearly match or havesimilarities with known cellular phenotypes and/or surface markers forthe cells. Further, functional profiles for TH1 cells (Type I helper Tcells), one of the major functional subsets differentiated from naïveCD4 T cells, have demonstrated marked heterogeneity as reflected bytheir various cytokine profiles. Using conventional methods based onflow cytometry, functional analysis of effector T cells have attemptedto delineate functional subsets of cells. It has been shown that each ofthese cellular groupings or classes can produce and release differentcombinations of cytokines within an immune response such as thatelicited by bacterial infection.

In various embodiments, the system and methods of the present disclosureprovide novel methods to measure cellular secretions and/or cell signals(such as those associated with immune cells or immune response).Analysis may be conducted at the single or few cell level (includingcell-cell interactions) where the effector level for a plurality ofcytokines may be evaluated per cell across a relatively large number ofcells in parallel.

As will be described in greater detail hereinbelow, the disclosedmethods enhance and improve the performance of analysis platformsaddressing particular issues related to distinguishing cellularretention regions or areas, identifying and classifyingcells/particulates, addressing systematic errors, and automatinganalysis workflows to enable high throughput sensitive and accurateassessments required to analyze many hundreds, thousands, millions, ormore discrete cells or samples in parallel. Such methods mayadvantageously be used to improve existing analysis platforms andtechnologies to create valuable discovery and screening tools that maybe used to understand and survey many different types of cellularpoly-functionality, such as those implicated and correlated with immuneresponse.

FIGS. 1A/1B depict an exemplary high-level workflow 100 for performingsample analysis. The analysis may include evaluation of one or moresamples 102, 104 comprising a population or distribution of cells,beads, or other particles 106 harvested or collected from one or moreselected sample sources (for example, cells obtained from an individualor culture representative of a selected biological state, chemicallyresponding to a disease, pathogen, or therapeutic treatment). Accordingto the present disclosure, where reference is made to one or morepopulations of cells, beads, or particles as comprising the selected ordesired sample to be analyzed other types of particulates associatedwith analytes to be detected and evaluated may also comprise thesample(s). Furthermore, the sample(s) may comprise various distinct ordifferent populations of cells, beads, or other particles forming one ormore mixed populations of materials to be analyzed collectively. It willbe appreciated that references to “cells” or “beads” are intended asexemplary of classes of particles that may be analyzed for associatedanalytes. Thus, the present teachings can be applied to a wide varietyof different types and compositions of particulates 106 withoutdeparting from the scope of the present teachings.

As referred to above, the cells/particulates 106 of the sample(s) 102,104 are evaluated for constituents associated with or comprising aplurality of analytes 108. Analytes 108 according to the presentdisclosure may take many forms. For example, analytes 108 may comprisechemicals, compounds or materials that are labile, dispersed, diffusedor dissolved within an aqueous medium (for example a cell culturemedium) or a fluidic/semi-fluidic carrier that are associated, secretedor released by the cells/particulates 106. Analytes 108 may furthercomprise biomolecules or organic molecules such as nucleic acids,peptides, peptide fragments, cells surface receptors, nucleic acids,hormones, antigens, growth factors, proteins, antibodies, cytokines,chemokines, or other molecules. Analytes 108 may further comprise othertypes of materials or compounds such as ions or inorganic chemicalsassociated, secreted or released by the cells/particulates 106.

According to various embodiments, the apparatus and methods of thepresent disclosure are particularly well suited for the analysis ofsmall concentrations of analytes 108 associated with discrete single orfew-cells contained within or comprising the samples 102, 104. Suchanalytes 108 may include for example cell-membrane associated proteins,cytokines, chemokines, or other biochemicals secreted or released by thecellular samples 102, 104 that are desirably analyzed in parallel in ahighly-multiplexed manner. Sample populations or cellular constituentsthereof may further be compared for similarities and/or differences inanalyte presence, expression, or abundance such as may arise from afirst control, normal, or untreated cellular population compared with asecond test, abnormal/diseased, or treated cellular population.

According to FIGS. 1A/1B, one or more samples 102, 104 containingcells/particulates 106 to be analyzed are harvested or collected in Step110. The cells/particulates 106 are then distributed and retained indiscrete portions in Step 120. Sample distribution is accomplished usinga substrate comprising a sample array 122 having a plurality of discretechambers, wells, troughs, channels or other features/areas that compriseretention regions 124. The retention regions 124 are further suitablyconfigured to contain, hold, or sequester at least a portion of thecells/particulates 106. In various embodiments, the array 122 used fordistributing the cells/particulates 106 comprises a plurality ofretention regions 124 such as wells, troughs, cavities, or depressionsformed in the sample array 122 that are suitably sized based on thedimensionality of the cells/particulates 106. Distribution of thecells/particulates in Step 120 may involve dispersing the sample in aselected fluidic volume such that a desired number of cells/particulatesare expected to be disposed in one or more of the retention regionsbased on the volume of the regions and the amount of fluid-containingsample to be located, disposed, or placed therein.

In various embodiments, the sample array 122 may comprise a plurality ofdiscrete retention regions each configurable to hold or position adesired number of cells/particulates. The sample array 122 according tothe present teachings may include a large number of retention regions124. For example, many hundreds, thousands, or millions of discreteretention regions 124 may be formed in the sample array 122. In oneexemplary embodiment, the sample array 122 may comprise a structure ofbetween approximately 1-10 cm in length and/or width having betweenapproximately 1,000-100,000 discrete retention regions formed therein.

In various embodiments, the sample retention regions 124 comprisemicrochambers/microwells having dimensions of approximately 0.01-5millimeters in length and about 0.5-100 micrometers in depth. In otherembodiments, the microchambers/microwells have a generally rectangularprofile with a length of approximately 0.1-2000 micrometers, a width ofabout 0.1-100 micrometers and a depth of about 1-100 micrometers. Thesize and shape of the microchambers/microwells can be configured for avariety of applications and cell/particulate types. For example, toaccommodate larger cell/particulates commensurately larger microwellsmay be desirably used. Additionally, for experiments involving more thana single cell/particulate to be evaluated in each microchamber/microwellthe size and/or dimensionality of the microchamber/microwell can beconfigured accordingly. Additionally, the density of the sample array122 can be flexibly configured, for example, with between approximately100-50,000 microchambers/microwells per cm².

Using dilution methods such as those based on Poisson statistics, anexpected distribution of cells/particulates 106 may be achievedresulting in at least a portion of the retention regions 124 receiving asmall number of cells/particulates or in various embodiments a singlecell/particulate 106. According to this approach for sampledilution/distribution some retention regions 124 may receive nocells/particulates 106 while others may receive more than the desiredamount of cells/particulates 106 (e.g. more than one or a few cells). Invarious embodiments, analysis of analytes 108 associated with samplepopulations of a few cells/particulates 106 and more particularly singlecells/particulates 106 includes identification of the presence andnumerosity of cells/particulates 106 within respective retention regions124. As will be described hereinbelow, the system and methods of thepresent disclosure are able to effectively image and distinguish desiredcell/particulate distributions for the various retention regions toidentify those which contain the desired amount or number ofcells/particulates.

Following sample distribution, the cells/particulates 106 are incubatedin respective retention regions for a selected period or duration inState 130. Incubating according to selected criteria or protocols, theretained cells/particulates 106 release analytes 108 into a surroundingvolume 132 within the discrete retention regions 124 (e.g. the volume inwhich medium containing discrete single or few cells/particulates areretained). The configuration of the sample array 122 allows thecells/particulates 106 of the sample(s) 102, 104 to secrete/releaseanalytes 108 into the volume 132. During the incubation period analyteconcentrations may build up and/or diffuse within the volume 132 of theretention regions 124.

Analytes 108 released into the volume 132 of the retention regions 124,for example by dispersal or diffusion throughout at least a portion ofthe retention region, may further associate or react with one or moredetection moieties 135 disposed or located about selected positions ofan analyte detection substrate 134 in Step 140. The detection moieties135 may comprise antibodies, nucleic acids, proteins or otherchemical/biochemical constituents that selectively react, couple,recognize, or otherwise distinguish and/or detect the presence of one ormore selected analytes 108. In general, the size and configuration ofthe retention regions 124 provides sufficient volume/area to allowanalytes 108 to be dispersed in a manner so that they may be discretelydetected by the detection moieties 135 through association with orpositioning about selected portions or regions (e.g. analyte detectionregions 136 where the detection moieties 135 are positioned or disposed)of the analyte detection substrate 134.

In various embodiments, secretions, biochemicals, cytokines, chemokines,proteins, peptides, peptide fragments, cells surface receptors, nucleicacids, hormones, antigens, growth factors or expressed proteinsassociated with the distributed cells/particulates 106 retained indiscrete retention regions 124 diffuse and distribute in the volume 132and become selectively associated with, captured, or retained by one ormore detection moieties 135 disposed or positioned in analyte detectionregions 136 associated with the analyte detection substrate 134 forminga detectible or discernable pattern, fingerprint, or barcode 142 whichwhen detected in State 150 forms a representation of the presence andcomposition of analytes 108 associated with respectivecells/particulates 106 contained within occupied retention regions 124.

In various embodiments, the analyte detection substrate 134 isconfigured with a plurality of analyte capture or detection moieties 135capable of detecting and distinguishing numerous analytes 108 (forexample between approximately 10-1000 different types of analytes). Thepositioning or association of the analyte detection moieties 135 inanalyte detection regions 136 on the analyte detection substrate 134 isconducted in such a manner so that simultaneous determinations ofanalyte 108 presences and/or abundance may be determined in a highlymultiplexed manner based on the pattern, fingerprint, or barcode 142. Invarious embodiments, the disposition or positioning of the analytedetection moieties 135 disposed or positioned in analyte detectionregions 136 on the analyte detection substrate 134 permit secretedand/or released cytokines, chemokines, proteins, or other cellularconstituents to be readily and individually classified based on thetypes and presence of the analytes 108.

The analyte detection substrate 134 may be comprised of a plurality ofcapture agents or detection moieties 135 each discretely or specificallyrecognizing a compound, chemical or biochemical of interest. The analytedetection moieties 135 may further be arranged in distinct orpositionally discrete features (e.g. analyte detection regions 136)about the analyte detection substrate 134 providing spatial separationbetween the various analyte detection moieties 135. Such spatiallyseparate arrangements of analyte detection moieties 135 provide forspatial encoding or patterning the detected analytes 108 in or aboutanalyte detection regions 136 that may be resolved based on a knowndistribution of the analyte detection moieties 135 in or about analytedetection regions 136 associated with the analyte detection substrate134. In various embodiments, the analyte detection substrate 134 maycomprise analyte detection moieties 135 arranged in the analytedetection regions 136 as a plurality of lines, spots or other discreteshapes or combinations of shapes.

Each analyte detection region 136 may further be comprised of one ormore types of analyte detection moieties 135 capable of beingdistinguished from one another through the use of different markers,labels, dyes, or other means generating distinct signals or otherspectrally separable characteristics that may be discerned upon imaging,for example, using different optical characteristics or wavelengthsduring imaging. In various embodiments, the analyte detection substrate134 comprises one or more duplicative, redundant, or repeating analytedetection moieties 135 or analyte detection regions 136 that may beused, for example, to provide multiple opportunities for analytepresence to be discerned and compared using signals, readouts, orpatterns obtained for the corresponding analyte detection regions 136.

A plurality of analytes 108 are desirably identifiable and/orquantifiable for the cell(s)/particulate(s) 106 associated withrespective sample retention regions 124. In various embodiments, theanalyte detection substrate 134 provides the ability to detect andresolve approximately 5-1000 or more different analytes 108 usingcorresponding analyte detection moieties 135/analyte detection regions136. The analyte detection moieties 135/analyte detection regions 136disposed or located on the analyte detection substrate 134 are typicallyordered or patterned in a manner that corresponds with or iscomplimentary to the sample retention regions 124 of the sample array122. Thus the length and width of respective analyte detection regions136 and the overall size and/or groupings of analyte detection regions136 may be variably configured based on the corresponding or availablearea determined by the sample retention regions 124.

As will be described in greater detail hereinbelow, signals resultingfrom the coupling or association of analytes 108 with correspondinganalyte detection moieties 135 associated with the analyte detectionregions 136 may be imaged forming signal fingerprints, barcodes or otherdiscernable patterns that may be analyzed and resolved to determineanalytes 108 present in respective sample retention regions 124 of thesample array 122. These analytes 108 may further be correlated withcell(s)/particulate(s) determined to be present in respective sampleretention region 124.

Detected analyte patterns may be resolved to the presence or quantity ofdiscrete analytes based on the spatial separation, spectral separation,or a combination of both spatial and spectral separation for the analytedetection moieties 135/analyte detection regions 136. The presentapparatus and methods may be used to resolve the presence of a pluralityof analytes 108 by multiplexed detection of 2 or more, 3 or more, 4 ormore, 5 or more, 10 or more, 20 or more, 40 or more, 50 or more, 100 ormore, 1000 or more analytes of interest. The image analysis apparatusand methods of the present disclosure thus provide the ability to detectlarge numbers of different analytes for large numbers of discrete singlecells/particulates or discrete few cells/particulates (co-located orco-positioned in corresponding sample retention regions 124) inparallel.

Analyte detection in Step 150 may comprise imaging the analyte detectionsubstrate 134 (either directly or following separation from the samplearray 122) to identify signals or markers (e.g. for example fluorescentand/or radioactive) representative of the presence of analytes 108 thathave coupled or associated with analyte detection moieties 135. Invarious embodiments, the analyte detection moieties 135 may compriselight-generating markers, energy-emitting markers, dyes, or other labels136 and utilize techniques such as antibody capture and ELISA analysisfor analyte detection.

Cells/particulates 106 and their corresponding analytes 108 arecharacterized in Step 160 by association of detected or discernedpatterns, fingerprints, signal readouts or barcodes 142 with respectiveretention regions 124. This processing includes identifying anddistinguishing discrete retention regions 124 associated withcorresponding areas for the analyte detection substrate 134. As will bedescribed in greater detail hereinbelow, this process may include anumber of operations that determine the position and/or orientation ofcorresponding cells/particulates 106, retention regions 124, anddetected or discerned patterns, fingerprints, or barcodes 142 in ahighly multiplexed manner. This analysis further provides a basis todiscern the secretions, biochemicals, and/or expressed proteins forindividual or a few cells in a highly parallel manner allowing otheranalysis such as phenotypic characterization to be performed from theresulting patterns of detected analytes to characterize individualsamples including quantitation of analytes, determination of expressionpatterns, categorization or grouping of samples into classes, and otheranalytical functions.

A particular challenge in high throughput single cell analysis resultsfrom the large number of very small features that must be rapidly andaccurately assessed in a substantially parallel manner. Such apparatusand methods should be capable of not only separating and retainingsingle cells in discrete regions but also should accurately discernlarge numbers of discrete low abundance analytes with a high degree ofconfidence. Sample and substrate features are typically diminutive insize necessitating high-resolution and careful imaging (for exampleusing high resolution instruments and/or microscopes). In variousembodiments, potentially complex analyte detection patterns,fingerprints, signal readouts or barcodes must also be discerned orassociated with individual wells or analytes 113 or analytes 115 toprovide the ability to simultaneously detect and evaluate multipleanalytes from each sample. Again the size of the analyte regions orareas and the closely spaced disposition of the analytes to be detectedpresents a particular challenge in terms of performing accurateanalysis.

FIG. 1C depicts an image of a portion of an exemplary sample array 122with magnified sub-portion 180 according to the present disclosure. Thesample array 122 may comprise many hundreds, thousands or more ofdiscrete sample retention areas 124 as shown in the magnifiedsub-portion 182. Each sample retention area 124 may further retain asingle cell or few cells (or similarly particles) 106. Individualcells/particulates 106 should not only be confidently discernable andassociated with a respective sample retention area 124 in which theyreside but also must be distinguished from various potential artifacts,noise and other sources of error. Various factors that may directlyinfluence imaging quality include for example, the size of the cellsversus potential contaminants, distinguishing particular cell-types inmixed populations, low quality/contrast in the image acquisition,deformities in the sample array, bubbles and other artifacts. As will beappreciated by those of skill in the art, imaging and resolvingcells/particulates in the presence of such confounding factors presentsparticular challenges.

FIG. 1C further depicts an image of a portion of an exemplary analytedetection substrate 134 with magnified sub-portions 280, 282 accordingto the present disclosure. The analyte detection substrate 134 maycomprise many hundreds, thousands or more of discrete analyte detectionregions 136 as shown in the magnified sub-portion 282. Each analytedetection region 136 may form an analyte pattern 142 corresponding tomarkers 135 detecting analytes 108 associated with cells/particulates106. Various factors that may directly influence imaging quality includefor example, the noise and background for regions where analyte markers135/patterns 142 are detected. As will be appreciated by those of skillin the art, imaging and resolving markers 135/analyte patterns 142 inthe presence of such confounding factors presents particular challenges.

FIG. 1C further depicts an exemplary overlay, merging, or association291 of corresponding images of portions of the sample array 122 and theanalyte detection substrate 134. Alignment and positioning ofcorresponding portions of the images of the sample array 122 and theanalyte detection substrate 134 provides a means to associate thesignals from various markers 135 detecting analytes 108 forming theanalyte patterns 142 with the cell/particulate 106 for which theanalytes 108 are further associated. Mechanisms for aligning andpositioning the corresponding portions of the images and analyzing theassociated data is described in greater detail hereinbelow.

As previously described and in various embodiments, features that are tobe desirably detected and associated during analysis 100 include thevarious cells or particles 106, the retention regions or microchambers124, and the patterns, fingerprints, signal readouts or barcodes. Thesmall size (for example, typically on the order of 20 micrometers orless in at least one dimension) and distinctive properties associatedwith imaging and resolving the various features presents numerouschallenges that are to be desirably overcome, especially when applyingautomated or semi-automated data acquisition and processing mechanismsrequired to achieve acceptable throughput and accuracy. The presentmethods and apparatus are capable of achieving requisite high-qualitydata for single- or two-cell/particulate chemical or biochemical profilereadouts, in particular, per assay chamber or retention region 124 whileimaging large numbers of these features including for example thousandsof cells/particulates 106, thousands of microchambers or retentionregions 124, and for hundreds of thousands if not millions of readoutsfor ease of detection and analysis.

For cellular analysis involving sample arrays with very small featuresizes imaging at very high resolutions may be impractical and/orinfeasible necessitating alternative methods for data acquisition andanalysis. One reason for this limitation is that imaging devices, suchas scanning microscopes and fluorimeters have resolution limitations.Upgrades to such hardware components can be very costly and integrationof newer components difficult to perform for standardized or calibratedworkflows that may be necessary in clinical and research settings.Additionally, addressing limitations in the field of view of imagingdevices such as scanning microscopes that may be used for featureresolution scans, by leveraging conventional capture and tilingapproaches is impractical or infeasible due to time, cost, andcomputational constraints. For example, imaging and analyzing a fullsample array 122 with the many thousands or more of retention regions124, cells/particulates 106, and analyte signal readouts could easilyinvolve capturing and tiling thousands of images which may be overlytime consuming or computationally prohibitive. The size of such a tiledimage may readily exceed hundreds of millions of pixels or more makingit very difficult to process and analyze. Alternatively, imaging atlower resolutions may be performed, however, such an approach maycontribute to diminished analysis quality and result in highquantization errors where each feature has a limited number of pixelsthat may prevent accurate assessments of cell type, noise correction,and/or signal resolution.

A further limitation of some workflows is that the optical/detectioncharacteristics and requirements of the cell/particulate sample arraymay be distinct and different from those leveraged to resolve/image thesecretion profiles or detected compounds. Simultaneous imaging on asingle device may therefore be impossible/impractical to conduct. Thusit may be desirable if not a prerequisite to perform multiple imagingsusing different signal/data acquisition devices (e.g. multiplemicroscopes/signal capture devices). These devices may not output datain identical formats (for example, at the same resolution ororientation) and therefore additional challenges are present to alignand associate detected secretion patterns with specific retentionregions and cells/particulates.

At the resolutions used to image both the sample array and chemicalsignal fingerprints the minute measurements required are potentiallyconfounded by many sources of imperfections. For example, even micronlevels of warping or distortion in the substrate surfaces as well asvery small contaminants should be accounted for to achieve sufficientlyhigh quality and accurate results. Additionally, imaging biological dataincluding cells often results in inconsistencies in cell morphology andobserved structure as well as the intensity and quality of the signal(secretion) readouts. The system, methods, and software of the presentdisclosure effectively address potentially observable variations such asthose exemplified above while distinguishing noise/background with ahigh degree of confidence.

According to various embodiments, desirable features of the presentteachings include the ability to automate data acquisition/extractionprocesses avoiding time-consuming and error-prone manual methods forobtaining data. Acquisition of assay data, even when attempting toleverage existing off-the-shelf/conventional image processing software,is both time-consuming (on the order of days per assay) and inaccurate.A further benefit of the present methods is that development of theintegrated and optimized imaging solution disclosed herein provides theability to confidently detect large numbers of very small features witha high degree of accuracy remaining economical and efficient time-wisewhile accommodating inconsistency of features, presence of noise,distortions or deformities in the substrate materials among otherchallenges. In various embodiments, the methods may be applied todevelop automated procedures that are simultaneously very accurate, butlikewise sufficiently practical to analyze, both from a time standpoint(for example less than 30 minutes of hands-on time) and a hardwarestandpoint (can be performed on a conventional computer).

As discussed above, the methods and apparatus for cellular analysisaddress many issues and shortcomings of conventional single cell imageanalysis solutions. FIG. 1D depicts a detailed analysis workflow 1000that may be used for imaging and resolution of sample array data (forexample single cell analysis arrays) according to the presentdisclosure. The workflow 1000 commences in state 1100 with collection ofa plurality of images associated with sample retention regions 124 ofthe sample array 122 and associated patterns, fingerprints, signalreadouts or barcodes 142 representing signal acquisitions from detectedanalytes 108 for various cells/particulates 106 disposed in the sampleretention regions 124.

In various embodiments, sample imaging and analysis operates byevaluation of groupings or collections of associated images and signalacquisition information for selected regions of the sample array 122. Aselected imaged region of the sample array 122 may represent asub-region 180/182 (see FIG. 1C) where a portion of sample retentionregions 124 from the sample array 122 are captured in an image.According to the present disclosure, rather than attempting to stitch arepresentation of the entire sample array together (a laborious,computationally expensive and error-prone process), each sub-region 180,182 may be independently imaged and evaluated to achieve high qualitydata and results rapidly using inexpensive commodity computing systems.Fiducials, labels, identifiers or other positional features(collectively, alignment markers) may be included, etched, or printed onthe sample array 122. The alignment markers may further be used tofacilitate orienting or identifying sample retention regions 124 throughor across multiple images and scans such that discrete sample retentionregions 124 of the sample array 122 may be associated withcells/particulates 106 residing or disposed therein. Thecells/particulates 106 may further be associated with the identifiedpatterns, fingerprints, signal readouts or barcodes (collectivelyanalyte patterns) for the cells/particulates 106 facilitating highaccuracy profiling of the detected released secretions and analytes 108.

As will be described in greater detail hereinbelow, a first image orscan (for example, a high resolution light field image) is obtained fora selected sub-region 180, 182 of the sample array 122. The highresolution image (such as may be obtained using a light microscope witha 10×-50× objective) may be used for initial cell/particulate detection.Using this first image the location, number, and distribution ofcells/particulates 106 in the various sample retention regions 124 ofthe sub-region 180, 182 of the sample array 122 may be discerned.Additionally, sample retention regions 122 containing a desired numberof cells/particulates 106 (for example single cells or two or moreinteracting cells) may be identified and distinguished from sampleretention regions 122 containing no cells/particulates 106 or more thana desired number of cells/particulates 106. The high resolution image isalso helpful for identifying and discriminating between relatively smalleffector cells/particulates 106 (for example in immunological assaysidentifying and distinguishing T-cells and natural killer cells fromother cells which may be present in the sample.)

To further aid in accurate alignment of images and/or alignment markersassociated with sample retention regions 124 and associated analytedetection regions 136, a second image for substantially the same orsimilar sub-region 180, 182 may also be obtained. The second image maycomprise a lower resolution image or other image type applying differentoptical parameters compared to the first image. Together, the first andsecond images for imaged sub-regions 180,182 may aid in the alignment ofvarious images/scans and provide improved cell/particulate detection andidentification accuracy compared to the use of single imagings. Invarious embodiments, the second image may be obtained in various mannersincluding using different optical settings such as adjusting contrast orfocal plane orientation, changing digital resolutions, applying adesignated fraction or percentage reduction in magnification compared tothe first image (for example, 1×-5× for a lower resolution image ascompared to 10×-50× for the first image or scan), and may be taken withdifferent illumination/microscopy methods, (e.g., darkfield, phasecontrast, fluorescence). In various embodiments, the analysis system maybe preconfigured with desired optical settings or may perform adetermination on-the-fly based on the data quality and experimentaldesign parameters as to what types of optical settings are applied toboth first and second images at each resolution setting. It will beappreciated that higher and/or lower resolutions and magnifications canbe readily adopted for use in the imaging processes. In variousembodiments, the magnification and other optical parameters for imagingwill be determined, at least in part, by the dimensions of the samplearray 122/sample retention features 124 and analyte detection substrate134/analyte detection regions 136 as well as the type ofcell/particulates 106 and analyte detection moieties used.

In addition to the first and second cell/particulate images obtained fora selected sub-region 180, 182, an additional scan or signal acquisitionis performed to obtain or identify the analyte pattern 142representative of the detected, released, or secreted compounds oranalytes 108 associated with the cells/particulates 106 present in thesample retention regions 122 of the sub-region 180, 182. In variousembodiments, the scan or signal acquisition information obtained foreach sub-region 180, 182 may be obtained on separate instruments and/orat different times providing for more flexible data acquisition.Additionally, first and second imagings may be based on differentoptical properties or characteristics from one another and as comparedto the analyte pattern scan. For example, the first and second imagingsmay be visible light-based images while the analyte pattern scanacquired from fluorescent signals.

In various embodiments, the various first and second images for selectedsub-regions 180, 182 are associated and accurately aligned usingfiducials or alignment markers that are detected and resolved inselected corresponding image sets. Furthermore, a separate set ofalignment markers (or additional imaging of the first set of alignmentmarkers at a different wavelengths using different markers, dyes, orlabels) may be imaged in both the sample array/particulate imaging andthe analyte detection scans. Alignment between the sample array imagesand the analyte detection scans may thus be performed to match orassociate the secretions or released analytes 108 detected from theanalyte pattern 142 associated with a selected sample retention region124 with the corresponding cell(s)/particulate(s) 106 located ordisposed therein.

It will be appreciated that many factors contribute to the overallquality of each image and the visibility of features/details in therespective images, Factors such as differences in lighting conditions,brightness, contrast, and focus can significantly affect imaging. Byobtaining multiple images in the manner described above, it will beappreciated that the combined results of the images may contribute toimproved identification reliability of both sample retention areas 124and cell(s)/particulate(s) 106 contained therein. Furthermore, due tothe generally small size of features (such as sample retention areaoutlines/borders and cell/particulate profiles in the images) havingmore than a single image and at different resolutions can prove to bevery helpful in resolving/imaging the sample array 122 and constituentstherein. As will be described in greater detail hereinbelow, variouspotential sources of noise and error that can otherwise act to obscureportions or entire features within a sub-region 180, 182 or result inmisregistration of cell(s) particulate(s) 106 and/or analyte patterns142 can be reduced or eliminated using the multiple imaging methods ofthe present disclosure. These methods can be helpful in resolving ordistinguishing usable data with increased accuracy and improvedquantification capabilities from the sample array 122.

FIG. 1E depicts an outline of the processes associated with the multipleimaging of sub-regions 180, 182 of the sample array 122 described inStep 1100 in FIG. 1D. High resolution images 1150 may comprise one ormore light field/bright field images 1155 of the microchambers/sampleretention regions 124 and associated or nearby first alignment markers(designated in the diagram by an exemplary “A”). Second images 1160 maylikewise comprise one or more light field images 1165 having a similarfield of view for the sample retention areas 124 and associatedcell(s)/particulate(s) 106 as the high resolution images 1155. First andsecond images may be aligned using alignment markers present on thesample array 122 and identified within the corresponding images toprovide the ability to orient first and second images 1155/1165 withrespect to one another.

Corresponding analyte pattern(s) 142 obtained, for example, through afluorescent imaging or scan 1170 comprise one or more scans 1175 ofanalyte patterns 142. The analyte patterns may be further resolved toindividual detected analytes 108 released or secreted bycell(s)/particulate(s) 106 present in the sample retention regions 124.The scans 1175 may further comprise discrete discernable patterns,fingerprints, detection patterns or barcodes 142 obtained by imaging atvarious selected wavelengths or using two or more modes of detection toform a collection of analyte patterns that are combined and evaluated toidentify detected analytes 108 associated with, released, or secreted bythe cell(s)/particulate(s) 108.

In various embodiments, a second imaging 1166 of the areas correspondingto the first and/or second images is obtained to identify one or moresecond alignment markers. In various embodiments, the second imaging isacquired at a different wavelength or emission spectrum for the secondalignment markers such that they may be readily distinguished from theone or more first alignment markers. For example, a fluorescent imagingor scan of the corresponding regions of the first and/or second imagesmay be obtained to distinguish a discrete marker set designated “B” inthe Figure. Using the image and associated alignment marker sets, themicroarray scan/analyte pattern (corresponding to the discernablepattern, fingerprint, or barcode 142 of analytes 108) includingcorresponding alignment markers “B” may be used to align or orient thefirst and/or second images with the corresponding microarrayscan/analyte pattern. Taken together using the processes describedabove, the various images and scans for respective portions on thesample array 122 may be desirably oriented and aligned to associatecell(s)/particulate(s) 106 with corresponding analytes 108 to enablehigh quality profiling and compound analysis.

In certain embodiments, one or more additional imagings 1167 of thecell(s)/particulate(s) 106 associated with the selected sub-region 180,182 imaged in the corresponding first and second images may be obtained.The cell imaging process 1167 may utilize similar or differentwavelengths or scanning configurations as the alignment marker imagingsor analyte detection scans. In various embodiments, the various cells orparticulates 106 may be labeled with fluorescent or light-emitting dyes,stains, or other identification means to resolve the location ordisposition of the cell(s)/particulate(s) 106 in various sampleretention regions 109 of the sample array 122. This information mayfurther be used to definitively identify the presence and/or number ofcell(s)/particulate(s) 106 in a selected sample retention region 108 andmay further be used to aid in associating the identifiedcell(s)/particulate(s) 106 with their corresponding discernable analytepattern 142 and corresponding released or secreted analytes 108.

In various embodiments, the dimensionality and other characteristics ofthe cell/particulate 106 being analyzed may determine the number and/ortype of images to be acquired for subsequent detection and analysis. Forexample, relatively large cells (for example THP-1 cells) may be imagedwithout staining and subsequent fluorescent imaging. For relativelysmall cells (for example CD4 cells) fluorescent stains may not berequired with multiple light field images acquired at variousmagnifications. For other small cell types, staining and fluorescentimaging as well as one or more light field images may be used. For mixedpopulations of cells and very small cells, surface markers may be usedand detected with appropriate imaging parameters to distinguish celltypes and/or to verify the presence of cells.

FIG. 1F panel (a) depicts an exemplary high resolution imaging of aselected sub-region 180 of the sample array 122 comprising a pluralityof sample retention regions 124. Cell(s)/particulate(s) 106 are furtherpositioned in various sample retention regions 124 (further shown inexpanded view). First and second positional/alignment markers 1168, 1171(e.g. markers sets comprising “A” and “B”) are further located about thesample retention regions 122 for alignment of images as discussed above.FIG. 1F panel (b) depicts an exemplary corresponding second image of aselected sub-region 180 of the sample array 122 comprising the sameplurality of sample retention region 124. Cell(s)/particulate(s) 106 arepositioned in the sample retention regions 124 at similar or identicallocations as visualized in the first image. Positional alignment markers1168 are visible about the sample retention regions 122 for alignment asdiscussed above. FIG. 1F panel (c) depicts the additional imaging ofcells/particulates 106 resultant from fluorescent labeling and imaging(step 1166 in FIG. 1E). Cell/particulate positioning and orientationwith respect to other cells/particulates may be preserved and comparableto that obtained for the light field images in panels (a) and (b) above.Cell/particulate positions are not necessarily required to be fixed ormaintained in a single position however as the sample retention region122 in which it resides may limit overall movement of thecell/particulate 106.

FIG. 1G panel (a) depicts exemplary signal and alignment scans showingmicroarray/analyte patterns associated with detected analytes 108released or secreted by cells/particulates 106. As described above theanalyte pattern 142 may be resolved to individual or discretely detectedanalytes 108 associated with cells/particulates 106 disposed or retainedin various retention regions 122. By correlating the analyte scans andunderlying detected analyte patterns 142 with the first and/or secondimages for the sample retention regions 122 with cells/particulates 106,individual cells/particulates or collections of cells/particulates canbe attributed to releasing or secreting multiple analytes 108 in aparallel and multiplexed manner.

According to the device imaging process (FIG. 1E, state 1175) analytepatterns 142 may be detected using multiple wavelengths or imaging/scanmodes. For example, multiple discretely and differentially tagged orlabeled analyte detection moieties 135 may be disposed in or co-occupygenerally the same analyte detection region(s) or area(s) 136 of theanalyte detection substrate 134. Different analytes 108 may selectivelybind or associate with the differentially labeled analyte detectionmoieties 135 and be separately resolved or detected on the basis of thetags or labels 135 associated with the analyte detection moieties 135.Discrete resolution of co-located analytes 108 in the same analytedetection region 136 may be determined on the basis of different emittedsignal/wavelengths associated with the tags or labels. Additionally,multiple images may be obtained at different wavelengths or usedifferent filters or optical settings to separately resolve the analytes108 in these regions. In certain embodiments, separate imagings may beacquired for the same analyte detection regions 136 to generatedifferent analyte patterns 142 based on the analytes 108 present and thetag, marker, dye or label associated with the analyte detection moiety135.

FIG. 1G panel (b) depicts detected second alignment markers 1171associated with the analyte detection substrate 134. The secondalignment markers 1171 may be resolvable in the device imaging processas previously described (FIG. 1E, state 1166). FIG. 1G panel (c) depictsdetected second alignment markers 1171 associated with the sample array122. The second alignment markers 1171 may be resolvable in the deviceimaging process as previously described (FIG. 1E, state 1166). Accordingto various embodiments, the detected alignment markers for the analytedetection substrate 134 and the sample array 122 may be used toassociate and orient the first and/or second images of the sample array122 (FIG. 1E, state 1168). Alignment between these imagings and scansthus provides the ability to associate respective analyte patterns 142with specific cell/particulates 106 releasing or secreting analytes 106within analyte detection regions 136 as will be described in greaterdetail hereinbelow.

FIG. 1H depicts a light field image (panel a) for a portion of anexemplary sample array 122 with sample retention regions 124 retainingsingle cells/particulates 106. FIG. 1H further depicts a correspondingfluorescent image (panel b) showing the cells 106 visualized by forexample by a stain indicating the presence of a selected surface markeron the cells/particulates 106. FIG. 1H further depicts a correspondingimage (panel c) for a portion of an exemplary analyte detectionsubstrate 134 with analyte patterns 142 corresponding to markers 135detecting analytes 108 associated with the cells/particulates 106. FIG.1H further depicts the merging of the various images (panel d) toprovide a mechanism to associate respective analyte patterns 142 withthe associated cells/particulates 106 whose analytes 108 were detected.

Referring again to FIG. 1D, collected images of the various sub-regions180, 182 of the sample array 122 and analyte detection substrate 134 areused in further processing steps according to the detailed analysisworkflow 1000. Initially, in state 1200 microchambers/sample retentionregions 124 of the sample array 124 are located and resolved forrespective images. Due, in part, to small feature size of the sampleretention regions 124 as well as potential artifacts and distortionsassociated with the structures themselves and resulting images, it isdesirable to conduct detailed evaluation of the various images andscans. These processes contribute to high quality analysis and aid inaccurate determination of the various analytes associated withrespective cells/particulates 106.

According to various embodiments, the sample array 122 may be configuredwith an ordered pattern or positioning of sample retention regions 124.An exemplary pattern may take the form of a two dimensional grid havingrows and columns as depicted in FIG. 1C. To aid in orientation anddetermination of sample retention regions 124 and cell/particulates 106residing therein, a regular patterning may be used where the dimensionsof the sample retention regions 124 are generally similar and typicallyapproximately equal to one another across various portions or sectionsof the sample array 122. Offsets between sample retention regions 124may likewise be configured as generally similar and typicallyapproximately equivalent to one another across various portions orsections of the sample array 122.

A principal operation during sample retention region identificationprocesses is to determine the relative position or location of theimaged sample retention regions. The graphical or pixel area of selectedsample retention regions 124 may then be used for determining respectivepositioning of cells/particulates 106 localized therein and alignmentwith analyte patterns 142. Improving the automated analysis capabilitiesof the imaging system is an important consideration to increasingoverall analysis throughput. Thus, it is desirable to increase ormaximize the number or proportion of sample retention regions 122 thatmay be detected.

In various embodiments, the regular ordering or patterning of the sampleretention regions 122 provide generally consistent or expecteddimensions and spacing that is helpful to leverage in automatedanalysis. Further, the associated detection procedures may be extendableor configurable to a number of different orientations, dimensions,spacings, and quantities of sample retention regions 124 on the samplearray 122. The system may also be configured for analysis of differentarray configurations depending on the particular assay or preference ofthe user.

The imaging and identification processes may further accommodate variousimperfections and deformities in the sample array 122 and associatedsample retention regions 124. In certain embodiments, the sample array122 is fabricated in such a manner that various deformities and/orimperfections may be present as a result of manufacture and/orprocessing. In some instances, the sample retention regions 124 may beslightly out of alignment or off-axis with respect to each other or havevariations from lot-to-lot or array-to-array. There may also exist minorvariations in sample retention region dimensions and spacing across thesample array 122. Further, various artifacts and debris may be presenton the sample array or associated with various sample retention regions124 that obscure sample retention region outlines or profiles.Additionally, the resultant images of the sample array and sampleretention regions may include imperfections such as blurry areas orbroken/distorted sample retention region outlines.

A more detailed process 400 for microchambers/sample retention region124 location and resolution associated with step 1200 is shown in FIG. 2. Additionally, FIG. 4 illustrates the principal operations 500 ofsample retention region identification in a pictorial manner. As shownin FIG. 4 , exemplary sample retention regions 124 may be subject tovarious imperfections 512 and artifacts 514 that obscure or confoundanalysis and are desirably addressed during automated image analysis.

The process 400 may be performed for selected, desired, or substantiallyall imagings of the sample array 122, for example using the highresolution light field images represented in FIG. 1F(a) and/or thesecond light field images represented in FIG. 1F(b). For each inputimage (410), representative of a selected sub-region 180, 182 of thesample array 122, a noise removal process (415/515) may be applied. Thenoise removal process may use various methods to identify noise andartifacts in the images including applying Gaussian difference methods.The results of the noise removal processing may exclude portions ofsample retention regions of low quality or that cannot be confidentlyresolved or distinguished from surrounding background, noise orartifacts and may also be used to counteract or address smallimperfections and image blurriness. Additionally, artifacts includingimaged dust and debris may be removed from the image and/or excludedfrom further processing and image analysis. Artifact removal may beconducted in such a manner so as to avoid high likelihoods of excludingcells/particulates from the image.

Following noise removal (415/515), an outline detection routine(420/520) may be performed to detect the peripheries, boundaries, oredges of the various sample retention regions 124 in the image 410.Edges, contours, and other shape-based characteristics for the sampleretention regions 124 may be determined by a number of methods includingapplication of Canny edge detection methods. The morphology or profileof the sample retention regions 124 may be evaluated as part of thisprocess to identify various distortions affecting selected sampleretention regions 124 such as dilations 532 and/or erosions 534. Leftunaddressed, these distortions may create difficulties in subsequentoperations and potentially result in inaccurate or erroneous analyteanalysis.

Application of the above-described processes helps identify and removepotentially problematic features and components of the image and may beused in connection with a filtering process (425/525). Excluding sampleretention regions that are affected by artifacts and noise as well asthose subject to significant deformities or other problems leaves asubset of filtered sample retention regions 542 having generallydesirable and/or tolerable characteristics that may be readily analyzedin downstream operations. Certain filtering process may include gatingout or excluding chambers by applying various criteria. For example,selected sample retention regions may be gated on the basis ofdiscrepancies or deviations in contour length, width, height, or as aresult of observed overlaps in the retention chambers.

It will be appreciated that exclusion of sample retention regionsaffected by noise, artifacts, and other issues results in a potentialloss of data and information associated with cell(s)/particulate(s) 106and detected analytes 108. In certain instances, a sample used in theanalysis may be particularly rare or valuable, the analytes 108 or thedetection moieties 135 used may be expensive or timely results arerequired which makes it important to capture as much information aspractical from a given analysis or run.

A notable enhancement to the image analysis routines of the presentdisclosure is the application of a recapture or fill-in procedure(430/530). According to this approach, the expected or predictedlocation of a sample retention region 124 may be determined using thepattern of regular orderings among sample retention regions 124. Forexample, for a sample retention region 124 that has not been located orwhich may have been excluded in previous operations the relativelocation of neighboring sample retention regions 124 may be determined.Using an iterative process, existing/identified sample retention regions544 immediately above and/or below a missing region 546 may be used tohelp identify the expected location of a sample retention region in themissing region 546. Attempts may further be made to recreate or add amissing sample retention region 546 on the basis of distance or spaceseparating adjacent identified sample retention regions 124. Outliningand resizing operations may also be used to determine the approximate orexpected position of sample retention regions 124. Taken together, wherethese operations are successfully applied, missing sample retentionregions may be added back or constructed into the image. Application ofthese processes therefore provides the ability to rehabilitate orrecover data from the image that might otherwise be lost or discarded.

Following the above-described operations, identified/remaining sampleretention regions in the various images may be sorted and organized(435/535). In various embodiments, the sample retention regions 124 in aselected image may be oriented and/or grouped with respect toneighboring sample retention regions. For example, vertically alignedsample retention regions may be labeled in a columnar manner andsorted/cataloged for ease of further analysis. Detected, located, and/oridentified sample retention regions 124 within the image may then beoutputted (440) completing the process (1200/400/500).

In addition to the microchamber/sample retention region identificationprocess 1200 described above, additional operations 1220, 1240 may beperformed to identify and locate associated alignment features ormarkers associated with analyte scans and corresponding cell/particulateimagings. Such processes may be performed for selected, desired, orsubstantially all imagings/scans of the sample array 122 and analytedetection substrate 136. For example, a first alignment featureidentification process 1220 may be performed using scans for thedetected second alignment markers 1171 associated with the sample array122 represented in FIG. 1G(c). A corresponding second alignment featureidentification process 1240 may be performed using scans for detectedsecond alignment markers 1171 associated with the analyte detectionsubstrate 134 represented in FIG. 1G(b). Location of the alignmentfeatures for scans representing portions of the sample array 122 and thecorresponding portions of the analyte detection substrate 134 may beperformed according to the steps 450 outlined in FIG. 3 and illustratedrenditions 550 in FIG. 5 .

Using selected input images/scans (455, 555), alignment featureoutlines, edges, or peripheries may first be detected (460, 560) usingCanny edge detection methods and/or applying line detection approachessuch as Hough transforms. In various embodiments, the alignment featurescomprise fine details, dimensions, and/or geometries 558 including forexample angles, turns, and profiles that may aid in confidentdetermination and resolution of the position of the alignment features.The alignment features, may further be resolved as a grouping of one ormore segments to aid in determination of portions of the alignmentfeatures that are clearly identifiable and portions which may beless-clearly resolved.

In various embodiments, alignment portions or segments that are clearlyresolved are filtered and grouped (465, 565). Filtering operations mayinclude identifying selected expected or imaged angles, determinedorientation and/or locations for portions of the segments and mergingsimilar or expected portions/segments while removing or excludingless-confidently evaluated alignment portions. The remainingsegments/alignment portions may be further identified, labeled, and/orordered (470/570) for example according to angle and positioning. Aportion of the operations may classify and/or label segment portionsbased on orientation or angle 572 as shown in FIG. 5 . Using known orexpected orientations for the alignment markers, portions that may bemissing or poorly resolved from the images/scans may be filled in orreconstructed (475/575). Comparisons between expected and actualintervals between alignment markers may be used to reconstruct missingregions or segments.

Using the actual and reconstructed alignment markers, additionalinterpolation operations (480/580) may then be performed, for example,between adjacent segments to obtain expected orientations andpositioning of analyte detection regions, flow lanes, andcell/particulate retention regions. Applying these operations torespective images for analyte detection regions and analyte patternsthus facilitate determination of expected and actual regions wherecell/particulates 106 and analyte detection moieties 134 reside.

Referring again to FIG. 1D, using located sample retention regions 122determined in step 1200, a subsequent stage 1300 of the analysis 1000determines potential cells/particulates 106 that may reside in thesample retention regions 122. The operations of cell/particulatelocalization 1300 address issues associated with variances that mayoccur between cells/particulates and facilitateidentifying/distinguishing desired cells/particulates 106 from bothother cells/particulates 106 that may reside in the same sampleretention region 122 as well as discriminating cells/particulates fromother materials such as dust or particles that may not be associatedwith analyte release or secretions.

In various embodiments, a cell may have an expected morphology, shape,or size when analyzed. For example, cells may be expected to beapproximately round and convex. Similarly, particulates may have knowndimensions and uniformity. During imaging, the image of a cell may havean expected profile, for example, with a generally darker outline orperipheral portion as compared to a generally lighter interior portion.Similarly, particulates may have other expected characteristics such asbeing uniformly illuminated or displaying various other opticalproperties. Cells in particular can be challenging to identify with ahigh degree of confidence due to variance between the physicalcharacteristics of individual cells. For example, cell size can vary,but may be assumed to reside within a selected range (for exampleapproximately in the range of 5-20 microns) where cell morphologyremains fairly consistent and independent of size. Further, in assaysinvolving few or single cell(s)/particulate(s), a majority of the spacewithin the sample retention regions 124 may remain open andcells/particulates 106 may be located in a variety of differentpositions within the sample retention regions 124.

According to the location identification step 1300, the occupancy andlocation (e.g. pixel area in a corresponding image) of one or morecells/particulates 106 within selected previously identifiedmicrochamber/sample retention regions 124 may be determined. Theseprocesses desirably accommodate cell-cell variances and deformities,variations in image quality, and help provide precise and accuratedetection. The detection processes 1300 are also desirably configured asextendable to different cell/particulate types having different physicalproperties and dimensions while able to handle large numbers of discreteidentifications associated with the large number of sample retentionregions 124 to be analyzed.

FIG. 6A illustrates an exemplary detailed process 600 forcell/particulate location identification. Commencing in state 610acquired light field images for the various sample retention regions 124may be used. Such images may include the first or second light fieldimages depicted in FIGS. 1F(a) and 1F(b). Cell localization 600 mayinclude establishing an upper and lower boundary or gradient threshold(step 615) used to determine criteria for including or excluding cellsbased on candidate identifications. In various embodiments, thresholdsmay be determined by computing an average or mean image intensitygradient for selected chambers/sample retention regions 124. Thereafter,upper and lower boundary thresholds may be determined using statisticalparameters or calculations such as identifying boundaries based on aselected number or percentage of standard deviations from the average ormean image intensity gradient. The image intensity gradient may then beused in subsequent analysis to include or exclude candidatecells/particulates in a readily automated manner.

To establish potential locations 660 where candidate cells/particulatesmay reside, one or more sample region masks or boundaries are identified(state 620). Depicted in the exemplary visualization 650 in FIG. 6B, thesample regions boundary mask 655 provides a convenient tool to delineateareas 658 that are to be searched or analyzed for cells/particulates106. The masks 655 may further serve to enumerate chamber or particleretention regions or areas to be considered and a logical flow may beapplied where each area 658 is analyzed separately (serially or inparallel) proceeding until all areas 658 have been processed. In state625, for each area 658 delineated by a boundary mask 655, putativecell/particulate identifications 662 may be made. The identifications662 may be based on selected criteria such as size/dimensionality offeatures located in the area 658 under consideration. These criteria mayfurther match or conform to expected proportionality or criteria thatcells/particulates 106 located in the areas 658 may be expected to meet.Cell/particulate identification may include one or more features withinthe respective areas 658 delineating one or more potentialcell/particulate identifications. Each putative cell/particulateidentification 662 may further comprise performing a gradient intensityanalysis where, based on established gradient thresholds determinedabove, candidate cells/particulates 106 may be identified as describedin greater detail hereinbelow.

To improve the accuracy of cell/particulate determination and/or toselect or discriminate between desired cells/particulates 106, a featuretemplate comparison is performed (state 630). FIGS. 6C and 6D depictexemplary cell template matching processes 670, 672 corresponding to thefeature template comparison process. In various embodiments, acollection or catalog of multiple cell/particulate template images 674may be obtained. The catalog may further be used to compare againstimages or regions having a putative cell(s)/particulate(s).

In various embodiments, cell templates 674 retrieved from a database arecompared to designated regions/areas corresponding to putativecells/particulates of interest for each sample retention area 124.Multiple imagings/renditions 678 of both the cell/particulate template674 and/or the selected region 676 under consideration may be made. Theimages 674, 676, 678 may further be normalized, scaled, and/or balancedby various operations (for example normalizing intensities to a range ofapproximately 0-1). In various embodiments, the multiple renditions 678may comprise various intensity gradients and/or magnitude for thevarious images (for example, representing changes in intensity ormagnitudes for the original and/or template image).

Applying a directional gradient imaging approach, selected or calculatedchanges in intensity for the images may be determined. As illustrated,three gradient images are depicted for the putative 676 and template 674images. Each of the three corresponding pairs of images may then becompared to each other (for example pixel by pixel) to determinesimilarities and differences between the putative 676 and template 674images. In various embodiments, a total intensity difference across thevarious images is computed and a similarity measure between the putativeregion 676 and the template 674 is determined. This information may beused in connection with designated thresholds to both include or excludeputative cell/particulate identifications as well as to further classifythe identifications as potential lower accuracy/false positiveidentifications 682 or definitive/higher accuracy/cell matchidentifications 684. State 630 is completed with the retention ofdefinitive/higher accuracy/cell match identifications 661.

To increase the percentage of productive cell/particulate analyteanalysis, potential lower accuracy/false positive identifications 682may be re-examined in state 635. For example, putative identificationsthat were not designated as definitive but nonetheless were highlyranked or likely cell/particulate candidates may be considered againstthe previously described criteria where additional cells/particulatesmay be included or retained as definitive/higher accuracy/cell matchidentifications 661.

As shown in FIG. 6B, sample region masks or boundaries 655 andcorresponding areas 658 may further be classified on the basis ofcells/particulates identified in the processes above. For example, fromthe number of cells/particulates associated with a selected area 658,the region may be designated or flagged to be excluded from furtheranalysis 667 (for example as containing multiple cells/particulates in asingle cell experiment or analysis). Other areas 658 containing adesired number or cells/particulates may be designated or flagged to beincluded in further analysis 668 (for example as containing a singlecell/particulate in a single cell experiment or analysis). Further,regions or chambers for which ambiguous results were obtained (forexample where a clear identification of the presence or absence of acell/particulate could not be made) may be flagged 669. In variousembodiments, ambiguous chambers or regions 669 may be subjected tofurther processing or examination 645. In some instances, such regions669 may be evaluated in connection with additional images/scans obtainedfor the sample array 122 such as the fluorescent cell stain marker imagedepicted in FIG. 1F(c). In some cases, additional processing in theaforementioned manner may improve the confidence of cell/particulateidentification and “rescue” associated regions which may be retained forfurther analysis.

The cell/particulate location identification process 600 depicted inFIG. 6A is completed with the output of image classifications in state649. Output from the process 600 may include information relating to theidentified cell/particulates 102 (e.g. location or positioning withinthe respective sample retention region, confidence determinations,cell/particulate classification or identification, etc.). Furtherinformation concerning the identified sample retention regions 122 mayadditionally be output including information relating to the number ofcells/particulates 106 contained in respective regions and the overallquality of region imaging.

FIG. 6E depicts an exemplary process for generating a cell/particulatetemplate library, database or population 674 for comparison inconnection with the aforementioned methods identified in associationwith FIGS. 6C and 6D. Using previous imagings or scans 691 of knowncells/particulates, one or more representative cell/particulate images692 may be identified. In various embodiments, identification of asubset of cells/particulates 692 having varied characteristics (e.g.different morphologies, sizes, intensities, or other properties) may beassociated with a selected type or class of cell/particulate 106. Thesubset 692 may be further refined and improved to form a representativecell/particulate subset 693 using data and information obtained fromexperimental results and analysis. In various embodiments, automatedmethods may select representative cell/particulate templates by applyingcriteria that consider similarities in morphology, experimental quality,confidence in match rates, and other criteria. Various representativecell/particulate subsets 693 may be collected from differentexperiments, literature descriptors and information, and other sources,improving the dataset over time. Additionally, in some instances, arepresentative cell/particulate subset may be determined in real timeusing images and data from a current experiment in which selectedcell/particulate images are used as a template or basis for comparisonagainst other cell/particulate images. In various embodiments,representative cell/particulate images are preferentially selected thatare dissimilar in appearance to other false positive or undesired cellsor particles that may reside in the sample retention regions 124.

Referring again to FIG. 1D, analyte patterns 142 associated with one ormore scans obtained from imaging of the analyte detection substrate 134are aligned and oriented (steps 1310, 1320) with selected images (e.g.first and second images for example corresponding to the high and lowerresolution images) of the sample array 122. As previously described,association of the sample retention regions 124 with the analytepatterns 142 provides the ability to determine analytes 108 secreted,released, or associated with cells/particulates 108 residing in selectedsample retention regions 124. In various embodiments, a plurality ofimagings of the analyte patterns 142 may be obtained, for example, usingdiffering spectral or optical characteristics during the imagingprocess. Multiple imagings in this manner may be used to resolveco-located or multiplexed analyte detection moieties 135 within the sameanalyte detection region 136 having discretely identifiable and/orspectrally separable labels or dyes providing the ability to distinguishmultiple analytes 108 from a particular region of the analyte detectionsubstrate 134.

FIG. 7A depicts a detailed workflow for alignment of images and scansdiscussed above. FIG. 7B further illustrates the operations applied toexemplary images and scans using fiducials/alignment markers 1168.According to various embodiments, the first and second images of thesample retention regions 124 corresponding, for example, to higher andlower resolution images, may have different regions of interest. Theimaging apparatus (e.g. a scanning optical microscope) may acquiremultiple sub-images (e.g. on the order of tens or hundreds) based ondifferent selected regions of interest. While these images may be tiledor aligned with respect to one another, such processes may be imperfectand results may vary significantly between high and lower resolutionimages.

To alleviate potential problems in alignment and to improve the overallaccuracy alignment markers 1168 are located about the sample array 122.In various embodiments, the alignment markers 1168 may appear adjacentor in proximity to selected sample retention regions 124 as previouslydescribed. Alignment markers may be present for each column of sampleretention regions 124 or disposed in various other positions about thesample array 122. In various embodiments, the number and/or size of thealignment markers 1168 may be varied in a repeating fashion or with aselected ordering further facilitating adjustment and alignment of theimages and scans.

In various embodiments, due, in part, to inconsistencies or variationsin the regions of interest, images may be desirably aligned to allowprecise matching or alignment of scans corresponding to the detectedanalytes 108 with the corresponding images identifying thecells/particulates 106. In some instances, due for example to tilinginconsistences as well as the small feature sizes (e.g. sample retentionregions 124 analyte detection regions 136) of the sample array 122 andthe analyte detection substrate 134 and further including potentialvariations, inconsistencies, and issues associated with the featuresthemselves, a single global alignment operation may be insufficient foraccurately aligning all regions in all images/scans. Consequently, it isoften desirable to provide mechanisms to locally align regions of eachimage/scan using subsets of alignment markers.

According to methods described in association with FIG. 7A first (e.g.for example high resolution) normal or light-field images may be readilyaligned with second (e.g. for example lower resolution) normal orlight-field images. In various embodiments, the process of image andscan alignment is facilitated by the lower resolution images havingsimilar fields of view and tiling as the analyte scans reducingalignment complexities. In step 710, one or more first (high) and second(lower) resolution images (corresponding for example to those depictedin FIGS. 1F(a) and 1F(b)) may be selected as inputs into the markerdetection and alignment process 700. A noise removal operation (step715) improves the image quality and removes artifacts. Such as processmay be accomplished using for example a difference of Gaussians approachas well as other noise removal methods. Input images are subdivided(step 720) such that each image comprises a range of selected or desirednumber of sample retention regions 124. For example, image subdivisionsmay be performed such that each sub-image comprises approximately asingle column of sample retention regions 124 (for example between 10and 100 or other selected amount).

For each sub-image, alignment markers 1168 may be identified by applyinga marker outline detection method (step 725). In various embodiments, aCanny edge detection approach may be utilized. Edge detection mayfurther comprise performing other quality assessments of the featurescontained in the images, such as, dilation and erosion morphologyassessments of the alignment marker regions as well as feature contourdetection. FIG. 7B illustrates an exemplary processing of sub-imageregion 755 comprising a plurality of alignment markers 1168 andassociated sample retention regions 124. The original image 755 may betransformed according to the contour detection and size gatingoperations described above to discretely identify or locate the variousalignment markers 1168. Following marker outline detection method (step725), false or erroneous marker identification may be performed (step730). To help insure actual or well resolved alignment markers are usedin subsequent alignment operations, the detected markers may beevaluated based on various criteria. For example, the dimensionality orpositioning of the alignment markers and sample analyte regions may beevaluated as well as the pixel areas associated with the variousalignment markers. Overlapping contours representative of poorlyresolved alignment markers may further be evaluated. In the variousalignment marker assessment operations described above, low qualityand/or poorly resolved alignment markers may be excluded from furtheranalysis therefore desirably avoiding potential downstream alignmentissues. In various embodiments, the number of alignment markers presentin a respective sub-image is desirably redundant or in excess of whatmay be used for image/scan alignment. Such redundancy provides greatertolerance and flexibility to the alignment processes.

In addition to identifying false positive, low quality, and/or poorlyresolved markers, the method 700 may include processes for identifyingthe expected locations for missing alignment markers or providingfill-in operations to correct for low quality and/or poorly resolvedmarkers (step 735). Following these processes, remaining alignmentmarkers 1168 may be more clearly identified by shading, coloring, orother methods to help insure the markers are clearly distinguishablewithin respective sub-images (step 740 and further illustrated in FIG.7B(c)). The resulting identified and processed alignment markers 1168and associated sub-images 765 may then be output for further processing(step 745).

Referring to FIG. 1D, as discussed above, similar operations 1310, 1320may be used for each sub-image 755 associated with the various imagesobtained in the high and lower resolution imagings (for example, FIGS.1F(a) and 1F(b)) resulting in processed images having high qualityalignment markers 1168 identified in each instance. Thereafter,corresponding first (e.g. high resolution) and second (e.g. lowerresolution) images may be aligned with respect to each other.

An exemplary method 770 for first and second image alignment is shown inFIG. 7C with corresponding exemplary illustration 792 for the alignmentprocess shown in FIG. 7D. Initially, sub-images with detected andvalidated alignment markers for corresponding first (e.g. highresolution) and second (e.g. lower resolution) images are received forprocessing (step 775). As previously described, multiple sub-images maybe desirably aligned corresponding to similar or identical regions ofinterest. Sub-image alignment proceeds selecting a first sub-image to beoverlaid oriented by a corresponding second sub-image applying one ormore offsets to aid in the alignment process (step 780).

Each offset may be representative of a shift in positioning therespective image frames with an offset distance determined, for example,by a number of pixels reflecting the offset distance or position. Foreach offset, detected alignment markers present in each sub-image areevaluated to determine the number of alignment markers that overlapbetween the respective sub-images. As will be described in greaterdetail, the number and degree of overlap between the alignment markersmay be used as a factor in determining the extent or quality ofsub-image overlap. For the various selected or determined sub-imageoffsets, the image offset with the largest pixel/alignment markeroverlap may be identified.

FIG. 7D illustrates the multiple offset process of step 780 where aselected sub-image 793 is overlaid/oriented with another associatedsub-image 794 for discrete offset intervals (a), (b), (c), and (d)corresponding to approximate alignment marker overlaps of 450 pixels,800 pixels, 1200 pixels, and 1800 pixels respectively. Using the imageoffset corresponding to the largest pixel overlap (e.g. (d) in FIG. 7D),sample retention regions 124 within the sub-image are assigned orassociated with the selected offset (step 785). This process may then berepeated for additional corresponding sub-images until substantially allcorresponding sub-images across the one or more first and second imagesis completed. The output of the process 770 provides details and valuesfor the alignment of the associated sample retention regions 124 in eachsub-image for each corresponding first and second images. The alignedimages may then be used in downstream processing and further associatedwith the analyte patterns as will be described in greater detailhereinbelow.

Referring again to FIG. 1D, detection and resolution of the discreteanalytes 108 secreted or released by cells/particulates 106 located inrespective sample retention regions 124 of the sample array 122 requiresassociation and alignment of the corresponding fingerprint, barcode, oranalyte patterns 142 formed on or associated with the analyte detectionsubstrate 134. As noted elsewhere, the small size and large number offeatures visualized across multiple images and scans make it importantto provide highly accurate means for orienting and aligning the imagesand scans with respect, to each other. Misalignments can lead to poorquality data analysis including missed and erroneous analyteidentifications. The methodologies described herein desirably avoid suchproblems and provide highly accurate means to associate and evaluate theimages and scans.

In various embodiments, addressable fiducials or alignment markers 1168are advantageously leveraged identifying and positioning associatedimages and scans with respect to one another, For example, alignmentmarkers 1168 associated with, positioned about, or aligned with sampleretention regions 124 of the sample array 122 (e.g, detected in images1F(b)) may be used in connection with corresponding or other alignmentmarkers 1171 associated with or positioned about analyte detectionregions 136 of the analyte detection substrate 134 (e.g. detected inimages 1G(b)).

It will be appreciated that a precise and/or unique alignment is desiredbetween the sample array 122 and the analyte detection substrate 134.Such alignment helps insure microchambers/sample retention regions 124are appropriately aligned with corresponding flow lanes/analytedetection regions 136. In this regard, various types, numbers and/orpositionings of alignment markers 1168, 1171 may help facilitateidentification of optimal alignment between the various images and scansof the sample array 122 and analyte detection substrate 134.

Alignment markers 1168, 1171 may further aid in resolving variations inscale or differences in imaging characteristics between scans and/orimages. For example, rotational and/or translational differences mayexist between the images and scans that are desirably accounted for andcorrected to improve alignment. To address these issues, accuratealignment marker detection is desirable and may be used to preciselyalign the various imagings. Several variables may be considered duringalignment marker identification and resolution including, for example, xand y translations or offsets, image/scan rotational angle differences,and image/scan scaling factors. Taking these factors into account,detected alignment markers may be used to orient the sample retentionregions 124 with analyte detection regions 136 with a high level ofaccuracy. FIG. 8A illustrates a detailed process 800 for association andalignment of the above-indicated features. FIG. 8B provides anillustration 850 of the processes described in FIG. 8A for an exemplarysub-image portion of sample retention regions 124 with correspondinganalyte detection regions 136.

In step 805, alignment markers detected from corresponding scans of theanalyte detection substrate 134 and sample array 122 may be collectedand associated. (For example FIGS. 1F(2 b) and 1F(3 b)). Alignmentmarkers may be positioned about the surfaces of the analyte detectionsubstrate 134 and sample array 122 in various manners to aid inorientation in various directions such as horizontally, vertically,and/or diagonally. By way of example, alignment marker details andalignment information may be obtained from preceding steps such asinformation generated during analysis of analyte detection regionlocation and identification (ex: FIG. 5 ). In step 810, an approximateposition and orientation of selected sample retention regions 124 may bedetermined using selected alignment markers (ex: diagonal markers). Instep 815, additional operations to refine the first determinedpositioning and orientation of the sample retention regions 124 may beperformed. Using one or more different sets of alignment markers (ex:horizontal markers and/or vertical markers) more precise positioning andscaling of the sample retention regions 124 may be performed accordingto one or more operations. The updated position and alignment details ofthe sample retention regions 124 may then be used to align against thepositioning for associated analyte detection regions 136. The results ofthe alignment for the respective images and scans are then output instate 820.

FIG. 8B provides an illustration of alignment 850 between sampleretention regions 124 and analyte detection regions 136. In panel 855, asub-image is depicted for detected flow lanes corresponding to theanalyte detection regions 136 that may be determined from positioning ofassociated alignment markers 856. In panel 860, a sub-image withcorresponding sample retention regions 124 is depicted with alignmentmarkers 857. It can be noted from the image that the relativepositioning of the two images appears slightly offset with respect toone another. Using the processes described above, the alignment markers856, 857 from the two sub-images 855, 860 can be leveraged to helpprovide more precise alignment between the two sub-images. Thus, insub-image 860 positioning and aligning using the two sets of alignmentmarkers 856, 857 provides an accurate approach to visualizing analytepatterns 142 formed at intersections of the sample retention regions 124and analyte detection regions 136. These regions of overlap furthercorrespond to positions where signals for analytes 108 are expected toreside based on the type of analyte detection moiety 135 disposedtherein as well as the presence of a detected analyte 108 secreted orreleased from one or more cells/particulates associated with the sampleretention chamber 124.

Referring again to FIG. 1D, in step 1340 the location and positioning ofanalyte detection regions 136 is determined. In various embodiments,location information for the analyte detection regions 136 may be aidedin part from previous analysis, calculations, and evaluations. Forexample, image analysis operations and information for alignment featureidentification in steps 1220, 1240 may be leveraged to further identifyassociated flow lanes/analyte detection regions 136 (see also FIGS. 3and 5 ). As shown in FIG. 5(f), located positions and orientations ofalignment features/markers may be used to further identify flowlanes/analyte detection regions 136 associated with detected alignmentfeatures/markers based on expected positioning of the flow lanes/analytedetection regions with respect to the alignment feature/markers.

As shown in the analysis workflow 1000, steps 1400, 1410, and 1420 maybe used to perform additional operations for cell/particulateidentification. For example, in cell-based assays single cell assaysand/or cell-cell interaction assays) and associated analyte 108 analysisit may be desirable to conduct additional operations to discriminatebetween various different cell types. Additionally, it may be desirableto evaluate or determine the physiological or biochemical status ofselected cells 106 for which analytes 108 are detected. According tovarious embodiments, one or more cell surface markers (e.g. for examplesurface markers corresponding to CD4+, CD8, and/or CD3) may be detectedand/or other stains or dyes evaluated (e.g. for example vitality stainsor dyes).

According to various embodiments, selected cell populations and cellcharacteristics may be identified, sorted, and/or distinguished bydetection of one or more representative cell surface markers orindicators of expressed membrane associated/secreted proteins. Singularcells as well as multiple cells 106 retained in selected sampleretention regions 124 may thus be exposed to various markers, dyes,stains, and/or reagents that are evaluated in addition to or inconnection with the analyte patterns 142 resulting from secreted orreleased chemicals, biochemicals or other cell-associated constituents.

During single cell or few-cell analysis, the above-describedidentification and characterization operations may further be used torefine and/or confirm cell location and positioning within a selectedmicrochamber or sample retention region 124 (step 1400). Such processesmay proceed using the various acquired images and scans evaluatingcharacteristic optical properties or cellular features associated withcells visible in the images and scans. For example, it may be observedthat when a cell is labeled, marked or stained with appropriate dyesand/or antibodies, the cell position may be determined by a generallyhigh fluorescence or signal intensity. Such signals may furtherindicate, for example, the presence of one or more corresponding anddiscernable surface markers (e.g. CD4+, CD8, and/or CD3) associated withthe cell or in the case of vital stains or other dyes some otherdiscernable characteristic (e.g. for example live vs dead cells).

Detected fluorescence or high signal intensities for cell surfacemarkers provide a good indication of the positioning of the cell whichis expected to be located close to or exactly in the same area as theobserved fluorescence or observed signal. In some instances, however,surface marker antibodies, dyes, stains, or other markers may generaterelatively noisy or diffuse signals that may result in areas ofrelatively high background within the sample retention region 124containing the cell 106. High background or noise may obscurevisualization or detection of cells/particulates 106 associated with thesample retention region 124. These high-background sample retentionregions 124 may be desirably identified, flagged, and/or excluded fromfurther analysis (step 1410).

Additionally, signal intensity or fluorescence may exhibit variancebetween cells/particulates 106 in different sample retention regions124, vary from experiment to experiment, and/or vary with the quality orcharacteristics of the stain, marker, or dye used. In some instances,there may be a population of cells/particulates 106 that do not exhibita detectible signal or possess only a weak signal despite having acorresponding surface marker or having been labeled with a selectedstain, marker, or dye. Furthermore, in instances where two or morecells/particulates 106 of the same type or general characteristicsreside in close proximity or next to each other, difficulties indistinguishing the origin of the fluorescence or signal may be observed.For cellular secretion, protein expression, and other types ofbioanalysis it may be important to accurately determine the cell typeand surface markers or other characteristic associated with eachdetected cell. Accordingly, it may be desirable to label potential orcandidate cell identifications as definite cells or false positive cellsbased on cell surface markers or other signal properties associated withthe cell (step 1420).

FIG. 9A depicts a detailed method 900 for cell/particulate positionidentification and discrimination. FIG. 9B further illustrates thepositioning and discrimination process 970 depicting an exemplarysub-set of sample retention regions 124 with cells labeled with surfacemarkers 985 (CD8 & CD3) for cell characterization and discrimination.The process 900 commences with receiving input images corresponding tocell/particulates 106 identifiable with dyes, markers, or labels thatcan be visualized for example by fluorescence detection (FIG. 1F(c)). Asdiscussed above, the markers may comprise one or more cell/particulatesurface markers imaged/scanned simultaneously or at different times andwhose data is aggregated through the alignment and positioningmechanisms described herein. Additional information may also be utilizedin the alignment process 900 corresponding to identifiedmicrochambers/sample retention regions, definite and potential cellscandidates, and aligned microchamber/sample retention regioninformation. (for example, obtained from the processes 400, 600, 700described in association with FIGS. 4, 6, and 7 respectively)

Initially, in step 910 an average background signal or noise ratio maybe determined (for example, to identify background fluorescence due todyes, markers, or labels used in the analysis). The average backgroundsignal may further be determined by evaluation of one or more sampleretention regions 124 lacking a cell/particulate 106 residing therein.In various embodiments, sample retention regions 124 lacking acell/particulate 106 may be expected to exhibit lower background orreflect average nominal background present throughout each of the sampleretention regions 124.

In step 915, sample retention regions or areas 124 are correlated withthe input images (ex: FIG. 1F(c)). This process 915 may further utilizeoffset information determined previously for first and second alignedimages ex: FIG. 1F(a), 1F(b)). In step 920, a microchamber or sampleretention region mask is determined to isolate or localize respectiveareas to be evaluated. A portion of this process 920 may includeidentifying an average pixel intensity or density in regions outside ofthe locations where cell/particulates 106 are detected or predicted todetermine background within a respective sample retention region 124.For example, an average pixel intensity may be determined for portionsof a selected sample retention region 124 by excluding detected orpotential cell areas, signals, or intensities. Thereafter, thebackground signal (e.g. fluorescence) for a selected microchamber sampleretention region 124 may be determined.

In step 925, the sample retention region 124 may be evaluated furtheridentifying high signal intensities in areas proximal to identified orpredicted cell/particulate locations. For example, the highestfluorescent signal residing around or in proximity to a cell/particulate106 may be determined for one or more surface markers or images. Takinginto account the background signal/intensity information identifiedabove and the maximal cell/particulate associated signal present, therelative position of cells/particulates 106 within respective images ofsample retention regions 124 may be determined with a high degree ofaccuracy. This information may be further correlated with the one ormore scans of analyte patterns 142 associated with the cell(s)particulate(s) 106 in the corresponding sample retention region 124.

In step 930, cell/particulate surface marker intensities may beevaluated to determine and quantitate detected markers. In variousembodiments, for each cell undergoing analysis the presence of one ormore surface markers detected or associated with the cell may be used todetermine the characteristics and/or status of the cell. Additionally,for instances where high background signals exist based on thedeterminations and calculations described above, associated sampleretention regions 124 may be flagged and/or excluded from furtheranalysis in step 935. Flagging or excluding sample retention regions inthis manner may aid in generation of high quality results and/or preventanomalous or erroneous interpretation of the sample data. Finally, instep 940 sample retention regions having acceptable background areoutput along with associated cell/particulate position andidentification characteristics including surface marker patterns ordescriptors (if present) for further processing.

FIG. 9B provides a pictorial representation 970 of the processesdescribed in FIG. 9A where in an exemplary single cell assay, surfacemarkers for CD3 and CD8 are imaged discretely and aligned. Imaging 972reflects detected signals for CD8 surface marker detection. A portion ofcells 106 in the imaging 972 exhibit sufficient signals to identify thepresence of the CD8 marker. In imaging 974, an exemplary light fieldimage of the same sample retention regions 124 indicate the presence thecells present in imaging 972 and other cells 106. Comparing, overlaying,or merging of the signals associated with the two imagings 972, 974 asdepicted by composite imaging 978 can be accomplished using alignmentmarkers and processes similar to those previously described.

Similarly, imaging 976 reflects detected signals for CD3 surface markerdetection. A portion of cells 106 in the imaging 976 exhibit sufficientsignals to identify the presence of the CD8 marker. Comparing,overlaying, or merging of the signals associated with the two imagings972, 976 as depicted by composite imaging 980 can be accomplished usingalignment markers and processes similar to those previously described.The composite imagings 978 and 980 can be further compared or overlaidto provide a final imaging that combines the data and signal informationfrom each of the previous panels. Taken together, cell position as wellas detected cell surface markers 985 may be determined. The presence ofdifferentially detected markers (CD8, CD3) indicate differingcharacteristics and/or expression patterns for the cells. Thisinformation can further be used to classify or group the cells anddiscriminate between different cell types and/or states. Suchclassifications and groupings may be further considered in relation torespective detected analyte patterns 142 discussed below.

Referring again to FIG. 1D, following positioning and alignmentoperations for the various images and scans associated with sampleretention regions 124 of the sample array 122 and the analyte detectionregions 136 of the analyte detection substrate 134, the analysisworkflow 1000 may proceed with locating and identifying analyte patterns142 associated with or attributable to selected cells/particulates 106(step 1430). FIG. 10A provides details of a workflow 1510 for secretionreadout determination. Additional secretion readout illustrations areprovided in FIG. 10B.

In various embodiments, a selected analyte pattern 142 corresponding toone or more detected analytes 108 expressed, secreted, or released bythe cell/particulate 106 comprise one or more discrete signals eachassociated with a respective analyte that are positioned about variousknown or expected locations with respect to the analyte detectionsubstrate 134. The manner of fabricating, positioning and/or depositingthe various analyte detection moieties 135/analyte detection regions 136about the analyte detection substrate 134 determines the orientation andposition of resultant detected analytes 108.

In various embodiments, two or more analytes may be detected inapproximately the same position or region but observed in differentscans. Positionally multiplexed analyte detection moieties 135 may thusbe resolved from one another by the type of signal emitted and/or thescan or imaging in which they appear. (For example, co-located orpositionally multiplexed analyte detection moieties may comprisediscrete labels or fluorophores that may be separately detected and/ordistinguished).

An analyte pattern 142 for a selected cell/particulate 106 may beidentified or “read” by associating a selected sample retention region124 with a corresponding series of one or more positions or regions 136on the analyte detection substrate 134 where analyte detection moieties135 are located. The disposition of the respective cell/particulate 106that results in detected analytes 108 may therefore be determined byevaluating one or more selected pixel area(s) or regions for the cellimages and the analyte detection scans where overlap occurs in a mannerdescribed in greater detail below. Signal intensity values identified inthe representative signal/scan images may be mapped to discrete pixelsand/or assigned numerical values representative of pixels in the imagingarea for ease of identification and processing. In various embodiments,identified signal intensities of a selected threshold may be averaged toobtain a value that may be associated with the detected analyte 108. Invarious embodiments, higher accuracy quantification of analytes 108and/or confirmation of the presence of selected analytes 108 may bedetermined using two or more regions of analyte detection/pixelanalysis. Scans/imagings for these regions provide discrete overlap intwo or more areas or locations between the sample retention regions 124and the analyte detection regions 136. In various embodiments, theobserved intensity values for each corresponding region of overlap maybe compared and/or averaged to yield a higher confidence identificationof respective detected analytes 108.

As shown in FIG. 10A, the analysis process 1510 may commence withacquiring detected flow lanes or analyte detection regions 136 that havebeen aligned with corresponding microchamber or sample retention regions124. In various embodiments, sub-images corresponding to detectedanalytes 108 may be utilized to identify regions of intensity where oneor more analytes are detected (see for example FIGS. 1G(a) and (b)).Corresponding flow lanes or sample retention regions 124 may then beused to overlay or merge the analyte scans to locate regions of overlap.As discussed above, overlap may be determined according to pixels in thevarious images and scans. Regions or areas of overlap may then beassigned to corresponding sample retention regions 124/analyte detectionregions 136 in a pairwise manner.

For assigned areas or regions of identified overlap the associatedintensity data or signal information may be extracted from each position(step 1525). Associated or corresponding signals from selected areas mayfurther be averaged to provide an output signal or readout thatcorresponds to the detected analyte 108. In certain instances, lowconfidence or outlier data or signals may be removed or flagged asdescribed in greater detail with respect to FIG. 11 below. Duplicate orredundant signal intensities associated with the same cell/particulate106 and analyte 108 pairings may further be compared and/or averaged forvalidity and/or confidence before generating a final analyte readout ofthe detected analyte 108. Thereafter, valid detected analytes 108,associated intensities, and other characteristics may be output (step1530).

FIG. 10B illustrates an example 1550 of the imaging and pixel comparisonprocesses described in FIG. 10A. As shown in sub-image 1555, a pluralityof analyte detection regions 136 (identified as “protein lanes”) may beoverlapping with a plurality of sample retention regions 124 (identifiedas “chambers”). According to the exemplary sub-image 1555, analytedetection regions 136 are generally vertically disposed overlaid ororiented with generally horizontally disposed sample retention regions124. Overlapping regions 1557 with observable signal intensities arefurther identified. Sub-image 1560 provides a magnified view of aselected overlap region between analyte “lane” 15 and sample “chamber”23. As depicted by the overlap region 1558, an analyte readout area maybe designated indicative of the presence of a detected analyte 108.Sub-image 1570 further illustrates pairwise readouts for two distinctoverlap regions 1572, 1574 corresponding to identical cell/particulate106 and detected analyte 108 regions. These regions may be evaluatedindependently and the results compared and/or averaged as describedabove.

FIG. 10C illustrates a further example 1551 of the imaging and pixelcomparison processes described in FIG. 10B above showing pairwisereadouts 1552 associated with marker patterns 142 for exemplary sampleretention regions 124. A plurality of analytes (e.g. cytokines) 108 maybe discretely detected simultaneously and resolved from the markerpatterns 142. As depicted in image panels (a) and (b), a plurality ofimages may be acquired using, for example, different wavelengths or withdiffering image acquisition characteristics to discretely identifymarkers 135 that may be co-located or positioned about the same analytedetection region 135. Images acquired with differing image acquisitioncharacteristics may further be overlaid or merged as shown in panel (c).

Referring again to FIG. 1D, following analyte/secretion locationidentification and analysis, a background signal evaluation and outlierdetection analysis may be conducted (step 1440), FIG. 11 depicts aprocess 1610 for gating, excluding, and/or flagging potentially lowconfidence, error-prone or outlier data and signals. As discussed abovein connection with FIG. 10 , various scans or images (or portionsthereof) may be subject to regions of high background and noise as wellas other potential artifacts that may diminish the quality of signaldata and results output by the analysis method 1000. To help avoidoutputting erroneous and low quality results from the analysis, variousquality checks or gating procedures 1620 may be applied to the outputanalyte identifications, readouts, and associated image/scan dataobtained from step 1530. This data may be first received for qualityreview (step 1625) where one or more quality checks 1620 are applied.

One exemplary quality test 1630 may comprise an assessment of image/scanquality based on pixel information. A Grubb's test may be performed toexclude or flag pixels in the images/scans that are identified aspotential outliers. In various embodiments, pixel data corresponding toimages/scans for portions of the sample retention regions 124overlapping with the analyte detection regions 136 (exemplified in FIG.10B) may be considered as well as other regions of pixel data. Pixeldata failing the Grubb's test may serve as an indicator that associatedimage/scan data in these regions are suspect and results/readouts may beexcluded or flagged for further review.

Another exemplary quality test 1635 may comprise evaluating pixel areasor sizes of regions where intensity information was extracted andevaluated. Size thresholds may be applied to the image/scan data andanalyte results/readouts associated with pixel areas that fall below ordo not meet the selected threshold excluded or flagged for furtherreview. As one example, insufficient overlap between an analytedetection region 136 and an associated sample retention region 124 mayresult in insufficient pixel information to accurately determine orassess the data. In regions with insufficient overlap as determined bypreselected criteria the results/readouts may be excluded or flagged forfurther review.

A further exemplary quality test 1640 may comprise evaluating image/scandata to identify regions of high interstitial background. In variousembodiments, evaluation of images/scans in areas substantially adjacentor proximal to identified analyte detection regions 136 and/or sampleretention regions 124 (for example, above, below, to the right, or tothe left) may indicate one or more of the interstitial regions exceed athreshold associated with the analyte signals. Such a result may suggestthe associated data/readout is indicative of a false positive result.Consequently, such results/readouts may be excluded or flagged forfurther review.

Another exemplary quality test 1645 may consider observed analyteresults associated with selected sample detection chambers 124. Invarious embodiments, sample retention chambers that are not associatedwith at least one reliable analyte identification/readout for selectedanalytes may be excluded or flagged for further review. In someinstances, at least one reliable readout should be obtained from two ormore associated analyte imaging areas.

Following application of one or more of the various gating and qualityassessment checks described above, the resulting analyte data/readoutsmay be considered valid and output (step 1650). It will be appreciatedthat applying various quality assessments desirably improve the overallconfidence and quality of the data analysis. Additional qualityassessments may thus be applied to further enhance the output analytedata/readout quality.

Referring again to FIG. 1D, following exclusion of high backgroundsignals and associated analyte data, data analysis 1000 may proceed withquantification of signals for analytes 108 detected in one or moreanalyte detection regions 136 (step 1450). FIG. 12A illustratesexemplary imagings 1610, 1620 depicting a portion of selected sampleretention regions 124 with intersecting analyte detection regions 136.Shown in image panel 1610, sample retention regions 124 (identified as“zero-cell-chambers” chambers 3, 5, 22) that have previously beendetermined to lack a cell/particulate 106 contained or residing thereinare identified. Signal intensities in the regions associated with thevarious intersecting analyte detection regions 136 (e.g. lane 15) may beleveraged to determine or characterize background signal intensities.

For example, the signal intensities associated with one or more of thesample analyte regions lacking a cell/particulate 106 may be evaluatedin the selected intersection regions 1630 to determine an associatedbackground signal. In various embodiments, background signals may bedetermined for one or more analyte detection regions 136 and/or for oneor more sample retention region 124. Background results may be furtherprocessed by combining, averaging, or other methods to determine anoverall background signal value to be applied to other signal intensitydata or applied individually to selected or corresponding signalintensity data for sample retention regions 124 located in proximity toa region 1630 where a background signal was determined.

As shown in image panel 1620, background signal intensities may then beapplied to the signal intensity data associated detected analytes 108for sample retention regions 124 that have been previously identified ascontaining at least one cell/particulate 106 (ex: chambers 19, 23). Invarious embodiments, background signal intensities may be used toevaluate the analyte signals/readouts associated with selected analytedetection regions 136 to further characterize the resultant detectedanalyte signals. For example, as shown in image panel 1620, signalintensities for a selected analyte detection region 136 (e.g. lane 15)may be evaluated for respective sample retention regions 124 (e.g.chambers 19, 23) and characterized with respect to background. Forexample, subtraction of background signal from associated signalintensities may be performed and corresponding regions characterized asabove-background (ex: 1640) or below-background (ex: 1645). Backgroundanalysis in this manner may further be used to qualify the presence orabsence of analyte 108 corresponding to the background-subtractedregions.

FIG. 12B depicts an exemplary scatterplot 1700 for an experiment inwhich a plurality of samples (e.g. cells 106) are evaluated for aplurality of analytes (e.g. cytokines 108). Analysis results are shownas a number or percentage 1710 of cells exhibiting analytesignals/readouts with respect to a background signal or backgroundthreshold 1720. The vertical axis 1730 provides relative intensityvalues or units for selected cytokines depicted on the horizontal axis1740. Background thresholds 1720 are determined for each cytokineindependently and cells characterized by the detected presence of ananalyte above-threshold 1750 or below-threshold 1760.

Analyte secretion or detection results depicted in the above-indicatedmanner provide a convenient method to evaluate overall expression,secretion, or presence of selected analytes for a potentially largenumber of cells simultaneously. Using this information, cells may beevaluated for analyte presence and/or response on a single cell basiswhile allowing potentially large populations of cells to be collectivelyprofiled to reveal insights regarding the behaviors, responses, orcharacteristics of the cells. Applying the disclosed analysis methodstherefore provides a practical means by which to assess for multipleanalytes including biochemicals, cytokines, and chemokines secreted fromthe same cell across significant numbers of cells simultaneously whilemaintaining sensitivity and accuracy to detect analyte secretions forsingle cells or cell-cell interactions.

FIG. 12C illustrates exemplary imaging results 1701 for merged images ofa selected subset of sample retention regions 124 with associated markerpatterns/readouts resulting from detected analytes 108 associated withone or more cells 106 contained within the sample retention regions 124.Pairwise analyte analysis results are shown as described previously andfurther associated with a cell number 1702 and cell type 1703.Evaluating detected analyte results from respective sample retentionregions 124 containing singular cell types 1703 (e.g. K-562 and NKcells) versus analyte results for sample retention regions 124containing two or more cells of different types can provide, forexample, information and insights into cell-cell interactions andassociated changes in cellular analyte response or cellular secretions.Additionally, evaluating analyte results for sample retention regions124 containing two or more cells of the same type can provide, forexample, information and insights into cell-cell interactions andfurther be used for quantitative analyte analysis correlated with thenumber of cells present in the chamber.

Referring again to FIG. 1D, the data analysis 1000 may includeadditional operations and tools for evaluation of populations ofdiscrete cells to determine correlative analyte (e.g.cytokine/chemokine) response and functional profiling of samples (step1460). In various embodiments, experimental data and results obtainedaccording to the disclosed methods may be further analyzed, compared,and associated providing powerful tools for discovery, screening,surveying many different types of cellular characteristics, responses,and functionalities, such as those implicated and correlated with immuneresponse. As shown in FIG. 13A and FIG. 13B, various methods and tools1800 for presenting and interpreting analyte data and results may beutilized. For example, in single cell experiments, evaluation ofpoly-functionality across a population of cells may be provided asoutput from the data analysis. Single cell expression data andinformation obtained from one or more experiments may be related toidentify patterns and trends in polyfunctional expression (e.g. theability to secrete multiple effector proteins/cytokines from the samecell). Secretion profiles corresponding to an exemplary polyfunctionalassay for selected cytokines is depicted in exemplary pie charts 1805,bar graphs 1810, and heat maps 1815. Additional correlations may bemade, for example, comparing cell types from multiple samples andcomparing detected analytes to known or established parameters todetermine analyte expression or secretion profiles, physiologicalcorrelations, disease states, and/or treatment response.

Referring again to FIG. 1D, the data analysis 1000 may includeadditional operations and tools to facilitate comparison and/orcorrelation of experimental analyte response results with otherinformation, for example, by comparison with a database of existingliterature/publications/datasets (step 1470). FIG. 14A depicts adatabase interface 1850 containing information that may be used toevaluate single cell experimental results providing a tool for inferencedetermination and discovery.

The database 1850 for example may comprise single cell secretion/analyteexpression information from previous experiments as well asliterature/published data. Exemplary experiment filters 1855,publication filters 1860, and data filters 1865 provide users with theability to focus on desired or relevant information which may be used toquery stored experimental data and publication information. Theexperimental filters 1855, for example, may allow selection or retrievalof data/information based on particular or selected informationalsources, diseases/conditions, subject types, cell types and/ortechnologies (used for sample analysis, such as flow cytometry,Enzyme-Linked ImmunoSpot (ELISPOT), the presently described technology,etc.). The publications filters 1860, for example, may allow selectionor retrieval of relevant literature/information based on particular orselected areas of interest, scientific categories, authors, publicationyear, and/or keywords. The data filter 1865, for example, may allowselection of data/information/literature based on particular or selectedcytokines or other analytes of interest. The data filter 1865 mayfurther provide functionality for data retrieval based comparisonsbetween cytokine/analyte secretion profiles or values as well asselections based on polyfunctional cytokine/analyte response. Variousmetrics can be chosen to automatically compare the similarity of theoverall cytokine/analyte secretion and/or polyfunctional profiles. Tofurther facilitate user interaction with the database tool, variousfilters may be auto-populated with search and data selection criteria.In conjunction with any user-selected filters, an overall determinationcan be made as far as the relevance or similarity of a catalogueddataset with the user's current dataset. In various embodiments, where aconclusive correlation cannot be determined, providing a small set ofrelevant past experiments/datasets can help guide or aid a user indetermining what subsequent tests may be informative to run, how atreatment could be performed relative to others, etc.

FIG. 14B depicts an exemplary results interface 1875 for displayinginformation from a selected single cell experiment. Results and relevantassociated datasets 1880 may be selected and examined in more detailwith convenient links to publications and associated data. A score orrank 1885 may further be attributed to the results 1880 based, forexample, on their similarity to an experimental or desired cell profileproviding details and correlations between the literature/publicationswith the obtained/experimental cellular response data.

FIG. 14C depicts an overall workflow 1900 for the above-described dataanalysis functionalities and database to provide single-cell profilingcapabilities in the context of immunological profiling analysis.Leveraging the throughput, sensitivity, and accuracy of the systems andmethods disclosed herein enables functional characterization of singlecells, populations of cells, and classes or types of cells providingadditional data gathering, evidence evaluation, and inferencedetermination functionalities to evaluate experimental results.

In various embodiments, and as shown in FIG. 14D the methods andprocesses described above and depicted in the associated figures may beimplemented using a platform 1950 comprising components for imageacquisition further comprising computational components for imageanalysis and subsequent analyte evaluation. The platform 1950 mayfurther comprise an integrated system, for example, a singular device orinstrument capable of performing each of the functions to output desiredanalyte results and associated data. Also, the platform 1950 may includediscrete or separate components comprising various instruments andcomputing components for performing various desired functionalities.

In various embodiments, sample arrays 122 and associated analytedetection substrates 134 are processed by an imaging apparatus 1952 thatacquires a plurality of images/scans. The imaging apparatus 1952 mayinclude one more more imaging devices including by way of examplemicroscopes, florescence detectors, microarray scanners, and/or otheroptical and signal detection apparatus. An image processor 1953comprising, for example, a computing device with suitable imageprocessing and data analysis software may be configured to receivesignal information, images, scans, and other data associated with theimaging process. The image processor 1953 may further evaluate andanalyze the information determine or associate analytes responsible forexhibited signals along with determining correspondingcell(s)/particulate(s) associated with the detected analytes.

The image processor 1953, for example, may determine correspondencebetween cytokine signals and detected single-cell chambers/microwellsdetected by the imaging apparatus 1952 and further associate thecytokine signals with one or more specific cells to generate acell-by-cell profile of secreted proteins for individual cells. Theimage processor 1953 may further quantify detected analytes andassociate this information with detected cells/particulates. The imageprocessor 1953 may further perform additional analytics that associatedetected analytes for the various detected cell(s)/particulate(s) acrossone or more analytes per cell/particulate. The image processor mayfurther determine cell/particulate functional responses andcharacteristics and leverage information contained in one or more datarepositories 1954 to perform further associations, determine outcomedata and compare against existing information/literature as describedabove.

The imaging and analysis methods may be implemented using softwarecomprising computer programs using standard programming techniques. Suchprograms may be executed on programmable computers each including anelectronic processor, a data storage system (including memory and/orstorage elements), at least one input device, and least one outputdevice, such as a display or printer.

In some embodiments, the code is applied to obtaining and analyzingimage and scan data (e.g., images and scans for sample retention regions124, analyte detection regions 136, and associatedcell(s)/particulate(s) 106 and analyte pattern(s) 142), to perform thefunctions described herein, and to generate output information (e.g.,assessment/quantitation of analytes, polyfunctionality determinations,and associated correlations/inferences), which may be applied to one ormore output devices. Each such computer program can be implemented in ahigh-level procedural or object-oriented programming language, or anassembly or machine language. Furthermore, the language can be acompiled or interpreted language. Each such computer program can bestored on a computer readable storage medium (e.g., CD ROM or magneticdiskette) that when read by a computer can cause the processor in thecomputer to perform the analysis described herein.

The speed, accuracy, and throughput capabilities of the system andmethods of the present disclosure are useful for cell populationanalysis at the level of single cell assays and cell signaling assaysbetween singular cells. In particular, the present teachings may beadvantageously applied to cell population analysis to discriminatebetween highly polyfunctional classes of cells such as immunologicalcells (e.g. B-cells, T-cells, macrophages, and other immuno-cell types).Immuno-cells are associated with the secretion of many cytokines percell and often exhibit significant differences between cytokinesecretion profiles for individual cells. Applying the teachingsdisclosed herein, clinicians and researchers are able to analyze celland protein secretion profiles in a sensitive and reproducible manner.Single cell cytokine secretion profiles (e.g. analyte readouts) may beanalyzed for one or more patients or samples simultaneously andevaluated against databases of immuno-cell cytokine responses toevaluate immune functionality, regulation and toxicity. As previouslydescribed, the system provides a high degree of automation forprocessing of various cellular images, analyte detection scans, andquerying databases of information to automate the single-cell functionalanalysis.

The composition of T-cell subpopulations (based for example on CD4/CD8ratios) as well as their associated effector/cytokine secretion profilesmay be correlative of clinical outcome and/or therapeutic response.Examples of where such analysis may useful include but are not limitedto immune cell response for patients with human acquired immunedeficiency syndrome (AIDS), as well as adaptive immune protectionresponses against pathogen infections.

In various instances, the same or similar cell type (e.g. for exampleCD4 or CD8 cells) may comprise highly heterogeneous populations withdynamic and evolving functional phenotypes. Such cells may be desirablyclassified at the single cell level by their immune effector functions(e.g. cytokine secretion profiles). Potency and durability of immuneresponse can further be correlated with polyfunctionality and thus it isdesirable to analyze multiple immune effector proteins from the samecell. In considering the nature of cytokine secretions, the degree ofimmune protection may be correlated with both the frequency ofpoly-functional immune cells (for example T-cells) secreting distinctcytokines simultaneously, as well as the quality and/or amount ofcytokine secretion.

Immune cells not only display distinctive and disparate immunologicalsecretion profiles but also influence activity in the micro-environmentwhere they reside. Cytokines such as the tumor-necrosis factor (TNF) areproduced by immune cells, and can improve the efficacy of T-cell primingand induce adaptive anti-tumor immunity once introduced into the cancerenvironment. Conversely, other cytokines have been associated with poorpatient outcomes, and have been reported to promote tumor growth andinhibit anti-tumor immune response. For example, imbalanced productionof interleukin 6 (IL-6), vascular endothelial growth factor (VEGF), ormacrophage colony-stimulating factor (M-CSF) inhibit adaptive anti-tumorimmunity by suppressing dendritic cell maturation and activatingregulatory T cells (Treg) to aid tumor cells in evadingimmune-surveillance. Transforming growth factor beta (TGF-β), which isabundantly expressed in many pathological conditions, heavily influencestumor growth and maintenance, as the cytokine has an important role informing a tumor microenvironment and facilitating angiogenesis. Applyingthe teachings of the present disclosure, accurate measures of thepreceding immunological cytokine actors at the single-cell level, acrossmultiple cells and cell populations/samples may be conducted. Cytokinesecretion profiles for individual cells or collections of cells may befurther evaluated and compared to existing information and literaturevales to create actionable patient immunological profiles and/or provideindicators or correlates of immune response and functional activationagainst cancers.

In other applications, polyfunctionality for diseased cells such asmalignant hematological cells or solid tumor cells may be evaluated onthe basis of cytokine secretions with various secretion profilescorrelated with pathogenesis. Cytokines that may be analyzed may includeby way of example, CCl-11, GM-CSF, Granzyme B, IFN-γ, IL-10, IL-12,IL-13, IL-17A, IL-17F, IL-18, IL-1β, IL-2, IL-21, IL-22, IL-4, IL-5,IL-6, IL-7, IL-8, IL-9, IP-10, MCP-1, MCP-4, MIP-1α, MIP-1β, Perforin,RANTES, sCD137, sCD40L, TGF-β1, TNF-α, TNF-β. Additionally, the systemand methods of the present disclosure may be desirably applied invarious clinical applications where characterization of effectorfunction across wide spectra or populations of single cells, forexample, in areas of cellular immunity and oncology tools is beneficialfor accurate diagnosis or therapeutic evaluation.

While the principles of the disclosure have been illustrated in relationto the exemplary embodiments shown herein, the principles of thedisclosure are not limited thereto and include any modification,variation or permutation thereof. Further, the examples set forth aboveare provided to give those of ordinary skill in the art a completedisclosure and description of how to make and use the embodiments of thedevices, systems and methods of the disclosure, and are not intended tolimit the scope of what the inventors regard as their disclosure.Modifications of the above-described modes for carrying out thedisclosure that are obvious to persons of skill in the art are intendedto be within the scope of the following claims. All patents andpublications mentioned in the specification are indicative of the levelsof skill of those skilled in the art to which the disclosure pertains.

The terminology used herein is for the purpose of describing particularembodiments only, and is not intended to be limiting. As used in thisspecification and the appended claims, the singular forms “a,” “an,” and“the” include plural referents unless the content clearly dictatesotherwise. The term “plurality” includes two or more referents unlessthe content clearly dictates otherwise. A number of embodiments of thedisclosure have been described. Nevertheless, it will be understood thatvarious modifications may be made without departing from the spirit andscope of the present disclosure. Accordingly, other embodiments arewithin the scope of the following claims.

What is claimed is:
 1. A method for spatial analysis of cells andmarkers on a substrate, the method comprising: (a) acquiring an image ofa plurality of cells distributed on one or more sample regions of asubstrate, the substrate comprising a plurality of alignment markersassociated with the one or more sample regions; (b) detecting putativealignment markers in the image; (c) identifying a set of alignmentmarkers from the putative alignment markers by (i) excluding falsepositive alignment markers, and (ii) filling in expected locations ofmissing alignment markers; (d) detecting the plurality of cells in theimage; (e) outputting a first sub-image comprising indicia of locationsof the set of alignment markers; and (f) outputting a second sub-imagecomprising indicia of locations of the plurality of cells.
 2. The methodof claim 1, wherein the image is a bright field image.
 3. The method ofclaim 1, wherein the locations of the set of alignment markers areindicated in the first sub-image by a color.
 4. The method of claim 1,wherein the locations of the set of the plurality of cells are indicatedin the second sub-image by a color.
 5. The method of claim 1, whereinidentifying the set of alignment markers comprises size gating.
 6. Amethod for spatial analysis of cells and markers on a substrate, themethod comprising: (a) acquiring a first image of a plurality of cellsdistributed on one or more sample regions of a substrate, and a secondimage of the plurality of cells distributed on the one or more sampleregions of the substrate, wherein the substrate comprises a plurality ofalignment markers associated with the one or more sample regions, andwherein the first image has a higher resolution than the second image;(b) detecting putative alignment markers in one or both of the firstimage and the second image; (c) identifying a set of alignment markersfrom the putative alignment markers by (i) excluding false positivealignment markers, and (ii) filling in expected location s of missingalignment markers; (d) detecting the plurality of cells in one or bothof the first image and the second image; (e) outputting a firstsub-image comprising indicia of locations of the set of alignmentmarkers; and (f) outputting a second sub-image comprising indicia oflocations of the plurality of cells.
 7. The method of claim 6, whereinthe first image and the second image are bright field images.
 8. Themethod of claim 6, wherein the locations of the set of alignment markersare indicated in the first sub-image by a color.
 9. The method of claim6, wherein the locations of the set of the plurality of cells areindicated in the second sub-image by a color.
 10. The method of claim 6,wherein identifying the set of alignment markers comprises size gating.